GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document provides an introduction to genetic algorithms. It describes genetic algorithms as probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance. The key components of a genetic algorithm are described, including encoding solutions, initializing a population, selecting parents, applying genetic operators like crossover and mutation, evaluating fitness, and establishing termination criteria. An example problem of maximizing binary string ones is used to illustrate how a genetic algorithm works over multiple generations.
Genetic algorithms (GAs) are optimization algorithms inspired by Darwinian evolution. They use techniques like mutation, crossover, and selection to evolve solutions to problems iteratively. The document provides examples to illustrate how GAs work, including finding a binary number and fitting a polynomial to data points. GAs initialize a population of random solutions, then improve it over generations by keeping the fittest solutions and breeding them using crossover and mutation to produce new solutions, until finding an optimal or near-optimal solution.
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
This document discusses genetic algorithms and provides an overview of their key concepts and components. It describes how genetic algorithms are inspired by Darwinian evolution and use techniques like selection, crossover and mutation to evolve solutions to optimization problems. It also outlines various parameters and strategies used in genetic algorithms, including chromosome representation, population size, selection methods, and termination criteria. A wide range of applications are mentioned where genetic algorithms have been applied successfully.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
The document introduces genetic algorithms, which are inspired by biological evolution. It describes how genetic algorithms use operations like selection, crossover and mutation to evolve solutions to problems in a way that is analogous to natural selection. It also outlines the basic components of a genetic algorithm, including representing solutions, initializing a population, evaluating fitness, and selecting solutions to breed new generations. Finally, it discusses some common applications of genetic algorithms to optimization problems.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document provides an introduction to genetic algorithms. It describes genetic algorithms as probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance. The key components of a genetic algorithm are described, including encoding solutions, initializing a population, selecting parents, applying genetic operators like crossover and mutation, evaluating fitness, and establishing termination criteria. An example problem of maximizing binary string ones is used to illustrate how a genetic algorithm works over multiple generations.
Genetic algorithms (GAs) are optimization algorithms inspired by Darwinian evolution. They use techniques like mutation, crossover, and selection to evolve solutions to problems iteratively. The document provides examples to illustrate how GAs work, including finding a binary number and fitting a polynomial to data points. GAs initialize a population of random solutions, then improve it over generations by keeping the fittest solutions and breeding them using crossover and mutation to produce new solutions, until finding an optimal or near-optimal solution.
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
This document discusses genetic algorithms and provides an overview of their key concepts and components. It describes how genetic algorithms are inspired by Darwinian evolution and use techniques like selection, crossover and mutation to evolve solutions to optimization problems. It also outlines various parameters and strategies used in genetic algorithms, including chromosome representation, population size, selection methods, and termination criteria. A wide range of applications are mentioned where genetic algorithms have been applied successfully.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
The document introduces genetic algorithms, which are inspired by biological evolution. It describes how genetic algorithms use operations like selection, crossover and mutation to evolve solutions to problems in a way that is analogous to natural selection. It also outlines the basic components of a genetic algorithm, including representing solutions, initializing a population, evaluating fitness, and selecting solutions to breed new generations. Finally, it discusses some common applications of genetic algorithms to optimization problems.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Genetic algorithms are inspired by Darwin's theory of natural selection and use techniques like inheritance, mutation, and selection to find optimal solutions. The document discusses genetic algorithms and their application in data mining. It provides examples of how genetic algorithms use selection, crossover, and mutation operators to evolve rules for predicting voter behavior from historical election data. The advantages are that genetic algorithms can solve complex problems where traditional search methods fail, and provide multiple solutions. Limitations include not guaranteeing a global optimum and variable optimization times. Applications include optimization, machine learning, and economic modeling.
This document provides an overview of genetic algorithms. It discusses how genetic algorithms are inspired by natural evolution and use techniques like selection, crossover, and mutation to arrive at optimal solutions. The document covers the history of genetic algorithms, how they work, examples of using genetic algorithms to optimize problems, and their applications in fields like electromagnetism. Genetic algorithms provide a way to find optimal solutions to complex problems by simulating the natural evolutionary process of reproduction, mutation, and selection of offspring.
The document discusses evolutionary algorithms and genetic algorithms. It defines evolutionary algorithms as computational models of natural selection and genetics that simulate evolution through processes of selection, mutation and reproduction to find optimal solutions to problems. Genetic algorithms are described as a class of stochastic search algorithms inspired by biological evolution that use concepts of natural selection and genetic inheritance to search for solutions. The key steps of a genetic algorithm are outlined, including initializing a population, evaluating fitness, selecting parents, performing crossover and mutation to produce offspring, and iterating over generations until a termination condition is met.
This document introduces genetic algorithms. It describes that genetic algorithms are adaptive procedures inspired by Darwin's theory of natural selection. The document outlines the key components of genetic algorithms, including chromosomes, genes, individuals, populations, fitness functions, selection, crossover, and mutation. It provides examples of where genetic algorithms can be applied, such as optimization problems. The document also discusses the general algorithm of genetic algorithms and their advantages and limitations.
Genetic algorithms are a search technique based on Darwinian principles of natural selection and genetics. They maintain a population of candidate solutions and evolve them through selection, crossover and mutation to find optimal or near-optimal solutions. Originally developed by John Holland in the 1960s, genetic algorithms have been widely applied to problems that are difficult to solve with traditional techniques. A genetic algorithm initializes a population, evaluates fitness, selects parents for reproduction, performs crossover and mutation on offspring, then iterates the process until a termination condition is reached.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
Genetic algorithms (GA) are a class of optimization algorithms inspired by biological evolution. GAs use concepts like natural selection and genetic inheritance to evolve solutions to problems by iteratively selecting better solutions. A GA encodes potential solutions as strings called chromosomes and uses genetic operators like crossover and mutation to generate new solutions, evaluating them to select the fittest ones. This process is repeated until a termination condition is reached, such as a solution meeting criteria or a fixed number of generations. GAs are well-suited for complex problems where little is known about the search space.
This document discusses genetic algorithms and their applications. It explains key concepts like genetic crossover, genetic algorithm steps to solve optimization problems, and how genetic algorithms mimic biological evolution. Examples are provided of genetic algorithms being used for tasks like predicting protein structure, automotive design optimization, and generating musical variations. Advantages and limitations of genetic algorithms are also summarized.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
This document provides an introduction to genetic algorithms, which are a class of computational models inspired by evolution. It describes how genetic algorithms use processes analogous to natural selection and genetics to arrive at optimal solutions to problems. The document outlines the key components of genetic algorithms, including representing potential solutions as binary strings, selecting parents based on fitness, recombining parents via crossover to create offspring, mutating offspring randomly, and replacing the population with the offspring. The goal is to evolve better and better solutions over many generations through these evolutionary processes of selection, recombination and mutation.
The document provides an overview of genetic algorithms, including their history, principles, components, and applications. Specifically, it discusses how genetic algorithms can be used to solve the traveling salesman problem (TSP) through permutation encoding of cities, calculating fitness based on total tour distance, and using techniques like order-1 crossover to preserve city order in offspring.
Exhaustive search involves systematically enumerating all potential solutions to a problem and checking if each one is a valid solution. It can solve problems that efficient algorithms cannot by simply trying every possibility, though this becomes intractable for large problems. The traveling salesman problem is an example where checking the length of every possible tour route between cities requires exhaustive search for even moderate numbers of cities. While slow, exhaustive search guarantees finding the optimal solution if allowed to run to completion.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
This paper proposes a parallel evolutionary algorithm to solve single variable optimization problems. Specifically:
- It presents a genetic algorithm approach that runs in parallel using a master-slave model, where the master performs genetic operations and distributes individuals to slaves for evaluation.
- The algorithm is tested on single variable optimization problems to find minimum/maximum values.
- Experimental results show the parallel genetic algorithm is effective at finding optimal solutions to these problems and represents an efficient parallel approach for optimization.
This document provides an overview of genetic algorithms. It discusses that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that is used to find optimal or near-optimal solutions to problems by mimicking natural selection. The document outlines the basic concepts of genetic algorithms including encoding, representation, search space, fitness functions, and the main operators of selection, crossover and mutation. It also provides examples of applications in bioinformatics and highlights advantages like being easy to understand while also noting potential disadvantages like requiring more computational time.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Genetic algorithms are inspired by Darwin's theory of natural selection and use techniques like inheritance, mutation, and selection to find optimal solutions. The document discusses genetic algorithms and their application in data mining. It provides examples of how genetic algorithms use selection, crossover, and mutation operators to evolve rules for predicting voter behavior from historical election data. The advantages are that genetic algorithms can solve complex problems where traditional search methods fail, and provide multiple solutions. Limitations include not guaranteeing a global optimum and variable optimization times. Applications include optimization, machine learning, and economic modeling.
This document provides an overview of genetic algorithms. It discusses how genetic algorithms are inspired by natural evolution and use techniques like selection, crossover, and mutation to arrive at optimal solutions. The document covers the history of genetic algorithms, how they work, examples of using genetic algorithms to optimize problems, and their applications in fields like electromagnetism. Genetic algorithms provide a way to find optimal solutions to complex problems by simulating the natural evolutionary process of reproduction, mutation, and selection of offspring.
The document discusses evolutionary algorithms and genetic algorithms. It defines evolutionary algorithms as computational models of natural selection and genetics that simulate evolution through processes of selection, mutation and reproduction to find optimal solutions to problems. Genetic algorithms are described as a class of stochastic search algorithms inspired by biological evolution that use concepts of natural selection and genetic inheritance to search for solutions. The key steps of a genetic algorithm are outlined, including initializing a population, evaluating fitness, selecting parents, performing crossover and mutation to produce offspring, and iterating over generations until a termination condition is met.
This document introduces genetic algorithms. It describes that genetic algorithms are adaptive procedures inspired by Darwin's theory of natural selection. The document outlines the key components of genetic algorithms, including chromosomes, genes, individuals, populations, fitness functions, selection, crossover, and mutation. It provides examples of where genetic algorithms can be applied, such as optimization problems. The document also discusses the general algorithm of genetic algorithms and their advantages and limitations.
Genetic algorithms are a search technique based on Darwinian principles of natural selection and genetics. They maintain a population of candidate solutions and evolve them through selection, crossover and mutation to find optimal or near-optimal solutions. Originally developed by John Holland in the 1960s, genetic algorithms have been widely applied to problems that are difficult to solve with traditional techniques. A genetic algorithm initializes a population, evaluates fitness, selects parents for reproduction, performs crossover and mutation on offspring, then iterates the process until a termination condition is reached.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
Genetic algorithms (GA) are a class of optimization algorithms inspired by biological evolution. GAs use concepts like natural selection and genetic inheritance to evolve solutions to problems by iteratively selecting better solutions. A GA encodes potential solutions as strings called chromosomes and uses genetic operators like crossover and mutation to generate new solutions, evaluating them to select the fittest ones. This process is repeated until a termination condition is reached, such as a solution meeting criteria or a fixed number of generations. GAs are well-suited for complex problems where little is known about the search space.
This document discusses genetic algorithms and their applications. It explains key concepts like genetic crossover, genetic algorithm steps to solve optimization problems, and how genetic algorithms mimic biological evolution. Examples are provided of genetic algorithms being used for tasks like predicting protein structure, automotive design optimization, and generating musical variations. Advantages and limitations of genetic algorithms are also summarized.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
This document provides an introduction to genetic algorithms, which are a class of computational models inspired by evolution. It describes how genetic algorithms use processes analogous to natural selection and genetics to arrive at optimal solutions to problems. The document outlines the key components of genetic algorithms, including representing potential solutions as binary strings, selecting parents based on fitness, recombining parents via crossover to create offspring, mutating offspring randomly, and replacing the population with the offspring. The goal is to evolve better and better solutions over many generations through these evolutionary processes of selection, recombination and mutation.
The document provides an overview of genetic algorithms, including their history, principles, components, and applications. Specifically, it discusses how genetic algorithms can be used to solve the traveling salesman problem (TSP) through permutation encoding of cities, calculating fitness based on total tour distance, and using techniques like order-1 crossover to preserve city order in offspring.
Exhaustive search involves systematically enumerating all potential solutions to a problem and checking if each one is a valid solution. It can solve problems that efficient algorithms cannot by simply trying every possibility, though this becomes intractable for large problems. The traveling salesman problem is an example where checking the length of every possible tour route between cities requires exhaustive search for even moderate numbers of cities. While slow, exhaustive search guarantees finding the optimal solution if allowed to run to completion.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
This paper proposes a parallel evolutionary algorithm to solve single variable optimization problems. Specifically:
- It presents a genetic algorithm approach that runs in parallel using a master-slave model, where the master performs genetic operations and distributes individuals to slaves for evaluation.
- The algorithm is tested on single variable optimization problems to find minimum/maximum values.
- Experimental results show the parallel genetic algorithm is effective at finding optimal solutions to these problems and represents an efficient parallel approach for optimization.
Genetic algorithms are a type of evolutionary algorithm that uses techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They are implemented as computer simulations that evolve solutions to optimization and search problems. Genetic algorithms use a population of abstract representations of candidate solutions called chromosomes. Operators like crossover and mutation are applied to chromosomes to generate new populations, with the fittest solutions most likely to reproduce and pass on their traits to the next generation. This process is repeated until a satisfactory solution is found.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm begins with a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive and are selected to reproduce, creating a new generation. This process is repeated until a termination condition is met. Genetic algorithms are inspired by biological evolution and can be applied to optimization and search problems.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm begins with a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive and less fit solutions are removed. This process is repeated until an optimal solution is found.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm initializes a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive to be selected for the next generation. This process is repeated until a termination condition is reached. Genetic algorithms are inspired by biological evolution and can be applied to optimization and search problems.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution, such as inheritance, mutation, selection, and crossover. They are commonly used to generate useful solutions to optimization and search problems by evolving candidate solutions over generations. Genetic algorithms work on a population of candidate solutions represented by chromosomes. They evolve toward better solutions through techniques like selection of the fittest solutions, crossover of parent solutions to create new solutions, and random mutation of new solutions. The algorithm terminates when either a maximum number of generations has been produced or a satisfactory fitness level has been reached in the population.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution, such as inheritance, mutation, selection, and crossover. They are commonly used to generate useful solutions to optimization and search problems by evolving candidate solutions over generations. Genetic algorithms work on a population of candidate solutions represented by chromosomes. They evolve toward better solutions through techniques like selection of the fittest solutions, crossover of parent solutions to create new solutions, and random mutation of new solutions. Genetic algorithms are applied to problems with large search spaces or when the solution is unknown.
This document discusses genetic algorithms and their components. It begins by explaining that genetic algorithms are a type of evolutionary algorithm inspired by biological evolution that uses techniques like inheritance, mutation, selection, and crossover. It then defines the key terms used in genetic algorithms, such as individuals, populations, chromosomes, genes, and fitness functions. The rest of the document provides more details on genetic algorithm components like representation of solutions, selection of individuals, crossover and mutation operations, and the general genetic algorithm process.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Evolutionary Computing is a research area within computer science. As the name suggest, it is a special flavour of computing, which draws inspiration from the process of natural evolution. The fundamental metaphor of evolutionary computing relates this powerful natural evolution to a particular style of problem solving – that of trial and error.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It presents the genetic algorithm process, including representation of solutions, initialization, evaluation, selection, and genetic operators. The algorithm is tested on various continuous and non-continuous functions, like polynomials, rationals, trigonometric, and those with asymptotes. The results show that the genetic algorithm can find the true maximum or one very close within a reasonable number of generations, even for complex multi-modal functions that are difficult to optimize with traditional methods.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
The document describes using a genetic algorithm to find the maximum values of single-variable functions. It discusses:
1) How genetic algorithms work by simulating biological evolution to optimize solutions.
2) Testing the genetic algorithm on continuous and non-continuous functions that are difficult to optimize with traditional methods, such as multimodal, non-differentiable functions.
3) The genetic algorithm was able to find maximum values close to the real maximum for complex test functions, demonstrating its effectiveness at optimizing these difficult single-variable functions.
In real world applications, most of the optimization problems involve more than one objective to
be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize
performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy
both objective functions and the optimal solution of one objective will not necessary be the best solution
for other objective(s). Therefore different solutions will produce trade-offs between different objectives
and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective
formulations are realistic models for many complex engineering optimization problems. Customized
genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to
these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each
of which satisfies the objectives at an acceptable level without being dominated by any other solution. In
this paper, an overview is presented describing various multi objective genetic algorithms developed to
handle different problems with multiple objectives.
The document provides information about optimization algorithms and genetic algorithms. It discusses that genetic algorithms are modeled after biological evolution and use processes like selection of fittest individuals, crossover to produce offspring for the next generation, and mutation. The key phases of a genetic algorithm are described as initializing a population, calculating fitness scores, selecting parents for reproduction, performing crossover on parents to create offspring, and applying occasional mutation. Genetic algorithms are suited for optimization problems as they can find good solutions efficiently.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution, such as inheritance, mutation, selection, and crossover. The algorithm begins with a randomly generated population that is evaluated and selected to produce a new generation, undergoing this process until a solution is found. Key components include individuals representing possible solutions, a fitness function to evaluate solutions, and genetic operators like crossover and mutation that are applied to selected individuals to create new solutions for the next generation. Genetic algorithms have been successfully applied to optimization and search problems.
Similar to Genetic algorithm fitness function (20)
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Computer network is a distributed system consisting of loosely coupled computers and other
devices. Any two of these devices, which we will from now on refer to as network elements or
transmitting elements, can communicate with each other through a communication medium. In
order for these connected devices to be considered a communicating network, there must be a set
of communicating rules or protocols each device in the network must follow to communicate wit
another device in the network. The resulting combination consisting of hardware and software is a computer communication network or computer network in short. Figure 1.1 shows a computer
network
Java is a general-purpose; object oriented programming language developed by Sun Microsystems of USA in 1991. This language was initially called “Oak” by James Gosling, but was renamed “Java” in 1995. Java (with a capital J) is a high-level, third generation programming language, like C, Fortran, Smalltalk, Perl, and many others.Java was initially designed to solve on a small scale could also be applied to the Internet on a large scale. This realization caused the focus of Java to switch from consumer electronic to Internet Programming.Java was designed for the development of software for consumer electronic devices like TVs, VCRs, Toasters, Microwaves ovens and such other electronics devices.Java is a first programming language that is not tied to any particular hardware or operating system. Programs developed in Java can be executed anywhere on any system.
Project Evaluation and Estimation in Software DevelopmentProf Ansari
Cost-benefit analysis
It mainly comprise two steps
Identify and estimating all of the costs and benefits of carrying out the project and operating the delivered application.
Expressing these costs and benefits in common units
We need to evaluate the net benefit, that is, the difference between the total benefit and the total benefit and the total cost of creating and operating the system.
We can categorize cost according to where they originate in the life of the project.
Stepwise Project planning in software developmentProf Ansari
The following activities are:
Identify objectives and practical measures of the effectiveness in meeting those objectives.
Establish a project authority
Stakeholder analysis – identify all stakeholders in the project and their interests
Modify objectives in the light of stakeholder’s analysis
Establish methods of communication with all parties
2.4
Entity Integrity Constraint:
It states that in a relation no attribute of a primary key (K) can have a null value. If a K consists of a single attribute, this constraint obviously applies on this attribute, so it cannot have the Null value. However, if a K consists of multiple attributes, then none of the attributes of this K can have the Null value in any of the instances.
Referential Integrity Constraint :
This constraint is applied to foreign keys. Foreign key is an attribute or attribute combination of a relation that is the primary key of another relation. This constraint states that if a foreign key exists in a relation, either the foreign key value must match the primary key value of some tuple in its home relation or the foreign key value must be completely null.
Normalisation in Database management System (DBMS)Prof Ansari
Normalization is a technique to organize the contents of the table for transactional database and data warehouse.
First Normal Form :
Seeing the data in the example in the book or assuming otherwise that all attributes contain the atomic value, we find out the table is in the 1NF.
Second Normal Form :
Seeing the FDs, we find out that the K for the table is a composite one comprising of empId, projName. We did not include the determinant of fourth FD, that is, the empDept, in the PK because empDept is dependent on empId and empID is included in our proposed PK. However, with this PK (empID, projName) we have got partial dependencies in the table through FDs 1 and 3 where we see that some attributes are being determined by subset of our K which is the violation of the requirement for the 2NF. So we split our table based on the FDs 1 and 3 as follows :
It is a semantic data model that is used for the graphical representation of the conceptual database design. The semantic data models provide more constructs that is why a database design in a semantic data model can contain/represent more details. With a semantic data model, it becomes easier to design the database, at the first place, and secondly it is easier to understand later. We also know that conceptual database is our first comprehensive design. It is independent of any particular implementation of the database, that is, the conceptual database design expressed in E-R data model can be implemented using any DBMS. For that we will have to transform the conceptual database design from E-R data model to the data model of the particular DBMS. There is no DBMS based on the E-R data model, so we have to transform the conceptual database design anyway.
The schemas as it has been defined already; is the repository used for storing definitions of the structures used in database, it can be anything from any entity to the whole organization. For this purpose the architecture defines different schemas stored at different levels for isolating the details one level from the other.
Different levels existing pat different levels of the database architecture pare expressed below with emphasis on the details of all the levels individually. Core of the database architecture is the internal level of schema which is discussed a bit before getting into the details of each level individually.
INTRODUCTION TO Database Management System (DBMS)Prof Ansari
shared collection of logically related data, designed to meet the information needs of multiple users in an organization. The term database is often erroneously referred to as a synonym for a “database management system DBMS)”. They are not equivalent and it will be explained in the next section.
Master thesis on Vehicular Ad hoc Networks (VANET)Prof Ansari
The increasing demand for wireless devices and wireless communication tends to research on self-organizing, self-healing networks without the interference of any pre-established or centralized infrastructure/authority [2]. The networks with the absence of any pre-established or centralized authority are known as Ad hoc networks [4]. Ad hoc Networks are the kind of wireless networks that uses multi-hop radio relay.There are many comparative studies and surveys that compare various ad hoc routing in VANET environment. The simulations performed in these comparative studies are very basic do not incorporate with a large number of nodes in real Vehicular Ad hoc Network environment. The main aim of our dissertation work is to firstly investigate the reactive and proactive routeing protocols than examine the performance of selected reactive routing protocols i.e. Destination Sequence Distance Vector Routing (DSDV), Ad hoc On-Demand Distance Vector (AODV), Optimized Link State Routing (OLSR) and Dynamic Source Routing (DSR)by taking three performance metrics like network load, throughput and end-to-end delay with varying number of mobile nodes or vehicle node densityOPNET: Optimized Network Engineering Tool (OPNET) is a commercial network simulator environment used for simulations of both wired and wireless networks [20]. Several different OPNET versions have been released over the last few years; the latest version of OPNET is the OPNET 16.0. At present OPNET is licensed under Riverbed technologies. It allows the user to design and study the network communication devices, protocols, individual applications and also simulate the performance of routing protocol. It supports many wireless technologies and standards such as, IEEE 802.11, IEEE 802.15.1, IEEE 802.16, IEEE 802.20 and satellite networks. OPNET IT Guru Academic Edition is available for free to the academic research and teaching community.
Master Thesis on Vehicular Ad-hoc Network (VANET)Prof Ansari
In present, many people during the public died each year in vehicle accidents, therefore in almost countries some safety data i.e. traffic lights & velocity limits are applied, simply however it is not a better solution. Also government and number of automation industries regarded that vehicular safety is real challenging task [1]. Then equally result, to enhance people traffic safety of a new progressed particular technology is formulated i.e. VANET [4]. It is progress type of MANET (Mobile Ad-hoc Network). VANET manages a network within which vehicles are act nodes and applied as mobile nodes to construct a robust infrastructure-less ad-hoc network. In Figure 1 illustrates the basic components of VANET architecture. It builds the network among Inter-Vehicle, Vehicle-to-Roadside and Inter-Roadside communicating networks [4]. Moreover, apart from accidental-safety and security types, there are also broad varieties of applications in VANET are available and potential that can extend passenger comfort like predictable mobility by GPS, web browsing and information modify and so on. Vehicular Ad-hoc Network (VANET) is a novel formulated form of Mobile Ad-hoc Network (MANET), where moving nodes are vehicles same automobiles, cars, buses etc [2].
Read/Write control logic:
The Read/Write Control logic interfaces the 8251A with CPU, determines the functions of the 8251A according to the control word written into its control register.
It monitors the data flow.
This section has three registers and they are control register, status register and data buffer.
The active low signals RD, WR, CS and C/D(Low) are used for read/write operations with these three registers.
When C/D(low) is high, the control register is selected for writing control word or reading status word.
HOST AND NETWORK SECURITY by ThesisScientist.comProf Ansari
Network management means different things to different people. In some cases, it involves a solitary network consultant monitoring network activity with an outdated protocol analyzer. In other cases, network management involves a distributed database, auto polling of network devices, and high-end workstations generating real-time graphical views of network topology changes and traffic. In general, network management is a service that employs a variety of tools, applications, and devices to assist human network managers in monitoring and maintaining networks.
SYSTEM NETWORK ADMINISTRATIONS GOALS and TIPSProf Ansari
The goal of network administration is to ensure that the users of networks receive the information and technically serves with the quality of services they expect.
Network administration means the management of network infrastructures devices (such as router and switches)
Network administration compromises of 3 majors groups:
1. Network provisioning
2. Network operations
3. Network maintenance
The VB6 IDE (Integrated Development Environment) is a very simple and fully featured IDE. If you start out programming in VB6 you may end up being too spoiled to ever appreciate a more complicated and less functional IDE like most C++ IDEs. One feature which sets VB6 apart from various IDEs is the simplicity of its approach to GUI (Graphical User Interface) design.
As a general rule: Play with it. You're very unlikely to break anything that matters, so just explore and experiment with the IDE, and you'll learn more.
ppt on blogging and In order to make money blogging you’re going to need to have a blog. While this is pretty obvious it is also a stumbling block for many PreBloggers who come to the idea of blogging with little or no technical background.blogging ppt presentation
Software Engineering is the set of processes and tools to develop software. Software Engineering is the combination of all the tools, techniques, and processes that used in software production. Therefore Software Engineering encompasses all those things that are used in software production like :
Programming Language
Programming Language Design
Software Design Techniques
Tools
Testing
Maintenance
Development etc.
These days object-oriented programming is widely being used. If programming languages will not support object-orientation then it will be very difficult to implement object-oriented design using object-oriented principles. All these efforts made the basis of software engineering.
E-Commerce is defined as the paperless exchange of business information using Electronic Data Interchange (EDI), electronic mail (e-mail), computer bulletin boards, Electronic Funds Transfer (EFT), and other, similar technologies.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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CHAPTER 3
BACKGROUND
3.1 GENETIC ALGORITHM
GA is a search technique that depends on the natural selection and genetics principles and
which determines a optimal solution for even a hard issue.
Genetic algorithms are normally a family of computational models which are
motivated by the biological evolution. These algorithms encode a powerful
solution to a particular problem on a simple chromosome i.e. data structure and use
genetic operators to these structures so as to preserve severe information.
Genetic algorithms are usually viewed as a function optimizer.
Genetic algorithms can be used to a broad range of problems.
For solving the issue by genetic algorithm the first step is to generate the
chromosomes population (random). Then these chromosomes are estimated in
such a manner that the chromosome which shows a better solution for the problem
will be provided more opportunities than the poorer solution.
The genetic algorithms are part of the evolutionary algorithms family, which are
computational models, motivated in the Nature.
GAs are more reliable as compared to other most search techniques because they need
only information related to the quality of the solution created by every parameter set
(objective function values) and not like other optimization techniques which needs
derivative information, or worse yet, entire knowledge of the parameters and problem
structure[9].There are some difference between GA’s and other optimization/conventional
searching algorithms [14] . They are briefly explained as follows
1) GAs work with a coding of the parameter set, not the parameters themselves. Thus GAs
can easily manage the discrete or integer variables.
2) GAs search within a points population, not a single point. Thus GAs can offer a
globally optimal solution.
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3) GAs employ only objective function information, not derivatives or other auxiliary
knowledge. Thus GAs can manage the non-continuous, non-smooth and non-
differentiable functions which are actually available in a practical optimization issue. 4)
GAs employ probabilistic transition rules, not deterministic rules, Although GAs appear to
be a good mechanism to solve optimization issue, sometimes the solution achieved from
GAs is only a near global optimal solution.
3.2 BASIC FEATURES OF GA
GA is a sub division of artificial intelligence.
AI (Artificial intelligence) is a computer science branch by which researchers
implement an intelligent computer system that has intelligence same as human
being.
GA comes in the category of search algorithms that are based on computer and are
random in nature. These algorithms are obtained from the natural theory of
“survival of the fittest” being specified by Darwin.
The mechanization of intelligent nature is a pre concern of this branch.
GA is also appropriate for complicated problems.
It generates the best of the best solutions.
The aim of GA is to increase the candidate solutions payoff in the population against an
objective function from problem domain. The scheme for the GA is to repeatedly use
surrogates for the mutation and recombination genetic processes on the population of
candidate solutions, where the objective function used to a decoded representation of a
candidate governs the probabilistic contributions a provided candidate solution can build
the subsequent generation of candidate solutions.
3.3 HISTORY OF GENETIC ALGORITHM
John Holland is assumed as the father of Genetic Algorithm. He discovered it in the early
1970's.and after that he and his students contribute much to the growth of this field.
Holland research was not concentrated on domain and optimization specific practical
problem but was on the adaptation concept as viewed in nature [6] and [7]. Other
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significant contributions were performed by Frantz in 1972 who inquired what were
known as Genetic Algorithm for Search and in 1971 Holstein inquired genetic plans for
adaptive control and function optimization. Several people involved biologists, are
amazed that life at the level of complexity that we realize could have emerged in the
relatively short time proposed by the fossil record [20].
Several human inventions were motivated by the natural selection. Genetic algorithm is
one of them. The main concept of this is the fittest survival or in other words it is called
natural selection. As evidently in nature the individual that has better chances for survival
will survive for a larger period of time. This in turn offers a better chance to create
offspring with its genetic material. In other words They can say that the fittest candidate
will survive and unfit will not. This force of nature is as known natural selection and on
this principle GA operates for solving optimization problems.
3.4BASICTERMINOLOGYofGA
Before beginning a discussion on Genetic Algorithms, it is essential to be familiar with
some basic terminology which will be used throughout this tutorial.
Population − It is a subset of all the possible (encoded) solutions to the given
problem. The population for a GA is analogous to the population for human
beings except that instead of human beings, we have Candidate Solutions
representing human beings.
Chromosomes − A chromosome is one such solution to the given problem.
Gene − A gene is one element position of a chromosome.
Allele − It is the value a gene takes for a particular chromosome.
Genotype − Genotype is the population in the computation space. In the
computation space, the solutions are represented in a way which can be easily
understood and manipulated using a computing system.
Phenotype − Phenotype is the population in the actual real world solution space in
which solutions are represented in a way they are represented in real world
situations.
Decoding and Encoding − For simple problems, the phenotype and
genotype spaces are the same. However, in most of the cases, the phenotype and
genotype spaces are different. Decoding is a process of transforming a solution
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from the genotype to the phenotype space, while encoding is a process of
transforming from the phenotype to genotype space. Decoding should be fast as it
is carried out repeatedly in a GA during the fitness value .
Figure 3.1: represent structure of Gene, Genome, Chromosome
Fitness Function − A fitness function simply defined is a function which takes
the solution as input and produces the suitability of the solution as the output. In
some cases, the fitness function and the objective function may be the same,
while in others it might be different based on the problem.
Figure 3.2: structure of Fitness Computation
Genetic Operators − These alter the genetic composition of the offspring. These
include crossover, mutation, selection, etc.
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3.5 OPERATORS OF GA
GA begins with random creation of initial population and then the selection, crossover and
mutation operations are conducted until best population is determined. Gas are practical
and simple algorithm and easy to be implemented in power system.
In other words, considering an initial random population generated and measured, genetic
evolution happens by means of three basic genetic operators [22].
1) Parent selection.
2) Crossover.
3) Mutation.
The descriptions of these genetic operators are provided below [23]:
1. Parent Selection/Selection Strategy:
The selection of parents to generate successive generations plays a significant role in the
GA. This permits the fitter individuals to be chosen more usually to reproduce. There is a
no. of selection techniques introduced in the literature [24].
In this technique, n individuals are copied from the population randomly and the best of
the n is introduced into population for further genetic processing. This process is repeated
until the mating pool is filled.
TournamentSelection
In K-Way tournament selection, It select K individuals from the population at random
and select the best out of these to become a parent. The same process is repeated for
selecting the next parent. Tournament Selection is also extremely popular in literature as
it can even work with negative fitness values.
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Figure 3.3: Tournament Selection Scheme
2. Crossover:
Crossover is a significant operator of the GA. The primary aim of crossover is to
reorganize the information of two different individuals and create a new one. It is a
structured, yet randomized method of exchanging formation between strings. It
encourages the exploration of new fields in search space. Cross swapping operator is used
on the chosen individuals. Here, two different cross sites of parent chromosomes are
selected randomly. The cross over operation is finished by exchanging the middle
substring between strings.
Figure 3.4: working of Crossover
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3. Mutation:
Mutation consists of securing the procedure of reproduction and crossover efficiently
without much loss of the potentially helpful genetic material. Mutation is by itself a
random walk through the string space and offers for occasional interference in the
crossover operation by introducing one or more genetic elements during reproduction.
This operation assures diversity in the genetic strings over large period of time and
prevents stagnation in the emergence of optimal individuals. Bit wise mutation changes 1
to 0 and vice-versa. The above specified operations of selection, crossover and mutation
are repeated until the best individual is detected.
Figure 3.5: Working of Mutation
3.6 BASIC STRUCTUREofGA
The basic structure of a GA is as follows −
Figure 3.6: Flow Chart of Genetic Algorithm
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3.6.1 ALGORITHM
A simple genetic algorithm of above discussion is provided as follow:
1. Create a population of binary string randomly
2. Compute the fitness for every string in the population
3. Generate offspring strings through reproduction, crossover and mutation operation.
4. Measure the new strings and compute the fitness for every string (chromosome).
5. If the search objective is fulfilled, or an allowable generation is achieved, return the best
chromosome as the solution; else go to step 3.
FLOWCHART
Figure 3.7 (a) Starting phase of Genetic Algorithm
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Fig 3.7(b): Flowchart of Simple Genetic Algorithm
The FF (fitness function) evaluation and genetic evolution take part in an iterative process,
which finishes when a maximum no. of generations is arrived, as illustrated in Fig.3.7.
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Figure 3.8 cost estimation in GA
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Figure 3.9 crossover and mutation in GA
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Figure 3.10 Fitness function estimation in GA
3.7 ADVANTAGES OF GENETIC ALGORITHM
Genetic algorithms differ from traditional search and optimization methods by some
significant points:
Genetic algorithms search parallel from a population of points. Therefore, it has
the ability to avoid being trapped in local optimal solution like traditional methods,
which search from a single point.
Genetic algorithms use probabilistic selection rules, not deterministic ones.
Genetic algorithms work on the Chromosome, which is encoded version of
potential solutions’ parameters, rather the parameters themselves.
Genetic algorithms use fitness score, which is obtained from objective functions,
without other derivative or auxiliary information
Genetic algorithms can be employed for a wide variety of optimization problems.
They perform very well for large scale optimization problems which may be very
difficult or impossible to solve by other traditional methods.
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3.8 VANET Routing Protocols
Routing is a mechanism to establish and to select a specific path in order to send data from
source to destination [14, 16]. There are various routing algorithm designed for ad-hoc
networks. Classification of various VANET routing protocols can be divided in two broad
categories: proactive or Table Driven Routing Protocols (DSDV, OLSR, FSR) and
reactive or On-demand routing protocols (AODV, DSR, TORA) that shown in figure 3.11.
In the next section describes a detail overview of various reactive routing protocols
(AODV, DSR).
Figure 3.11 VANET Routing Protocols
3.9 Reactive/On Demand Routing Protocols
Reactive routing protocols were designed to reduce the overheads by maintaining
information for active routes at each node [8]. This means that each node determined and
maintained routes only when it requires sending data to a particular destination. It using
two main mechanisms for route establishment: Route discovery and Route maintenance
[17, 25]. Route discovery mechanism uses two messages: Route Request (RREQ) and
Route Reply (RREP).
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Figure 3.12: Route Request Propagation in Reactive Routing Protocols
The basic approach is when a node needs to send a message to a particular destination, it
broadcasts the RREQ message in the network that shown in figure 3.12 When RREQ
message found a destination node then destination node send a RREP message to source
node that shown in figure 3.13.
Figure 3.13: Route Reply Propagation in Reactive Routing Protocols
Ad hoc On Demand Distance Vector (AODV): Ad hoc On Demand Distance Vector
(AODV) is a pure reactive routing protocol which is capable of both unicasting and
multicasting. In Ad hoc On Demand Distance Vector (AODV), like all reactive protocols,
it works on demand basis when it is required by the nodes within the network [8, 14].
When source node has to send some data to destination node then initially it propagates
Route Request (RREQ) message which is forwarded by intermediate nodes until
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destination is reached. A route reply message is unicasted back to the source node if the
receiver is either the node using the requested address, or it has a valid route to the
requested address that is shown is figure 3.14.
(a) (b)
Figure 3.14: AODV Route Discovery Process. (a) Propagation of the RREQ.
(b) Path of the RREP to the source.
Working of Ad Hoc On Demand Distance Vector Routing (AODV): In this type of routing
[14, 16] allows the communication between two nodes via intermediated nodes, if those
two nodes are not within the range of each other. To establish a route between source to
the destination, AODV using route discovery phase, along which Route Request message
(RREQ) messages are broadcasted to all its neighbouring nodes. This phase makes sure
that these routes do not forms any loops and find only the shortest possible route to the
destination node. It also uses destination sequence number for each route entry, which
ensures the loop free route, this is the one of the main benefit of AODV routing protocol.
For example if two different sources send two different requests to a same destination
node, then a requesting node selects the one with greatest sequence number. In the route
discovery phase several control messages are defined in AODV that are defined as
follows.
a) RREQ (Route Request): When any node wants to communicate with other node
then it broadcast route request message (RREQ) to its neighbouring nodes [14, 16].
This message is forwarded by all intermediate nodes until destination is reached.
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The route request messages (RREQ) contains the some information such as RREQ
id or broadcast id, source and destination IP address, source and destination
sequence number and a counter.
b) RREP (Route Reply): When any intermediate nodes received Route Request
(RREQ) message then it unicast the route reply message (RREP) to source node
either it is valid destination or it has path to destination and reverse path is
constructed between source and destination [14, 16]. Each route reply message
(RREP) packet consist of some information such as hop count, destination
sequence number, source and destination IP address.
c) RERR (Route Error): Whenever there is any link failure arises in the routing
process then route error message (RERR) is used for link failure notifications. The
route error message (RERR) consist of some information such as Unreachable
Destination node IP Address, Unreachable Destination node Sequence Number.
Routing in AODV: There are various mechanisms which are followed in AODV routing
approach:
a) AODV Route Discovery phase: To establish a route between source node to the
destination node, AODV using route discovery phase, along which the Route
Request message (RREQ) messages are broadcasted to all its neighbouring nodes
[14]. This phase makes sure that these routes do not forms any loops and find only
the shortest possible path to the destination node. It also uses destination sequence
number for each route entry that ensures the loop free route, this is the one of the
main benefit of AODV routing protocol. For example if two different sources
sends two different request to a same destination node, then a destination node
selects only that node having largest sequence number. In the route discovery
phase several control messages are defined in AODV protocol.
b) AODV Route Table Management: In AODV, Routing table management is
required to avoid those entities of nodes that do not exist or having invalid route
from source to destination. The need for routing table management is important to
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make communication loop free. It consists of following characteristics to maintain
the route table for each node.
• Destination IP address
• Total number of hops to the destination
• Destination sequence numbers
• Number of active neighbours
• Route expiration time
c) AODV Route Maintenance: In AODV, when any node in the network detects that a
route is not valid anymore for communication it delete all the related entries from
the routing table .And it sends the Route reply message (RREP) to all current
active neighbouring nodes to inform that the route is not valid anymore for
communication purpose.
Dynamic Source Routing Protocol (DSR)
Dynamic Source Routing is a reactive routing protocol that is based on the concept of
source routing [8, 16]. Source routing means source has the complete knowledge of entire
route to the destination before transmitting data. In DSR each node maintains a route
cache where it records all possible learned routes. It using two main mechanisms: Route
discovery and Route maintenance.
Route Discovery: Whenever a source node wants to send a data packet to destination node
in the network, it first looks in its Route Cache to find a valid hop sequence to the
destination [1].
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Figure 3.15: Route Request Propagation in DSR
If such a route exists, the source node attaches to the packet header the complete route to
the destination and forwards the packet to the next node. The next node checks the packet
header and forwards the packet to the next node. The process terminates when the packet
reaches the destination. If the source node cannot find a valid hop sequence to the
destination in its Route Cache then it initiates a route discovery process [17].
In route discovery process a route request (RREQ) message is broadcasts to all its
neighbouring nodes, adding a unique request ID to each request to prevent
Figure 3.16: Route Reply Propagation in DSR
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Figure 3.15 shows the RREQ message propagation in the network. And figure 3.16
shows the propagation of RREP message that carries the entire hop sequence.
a) Route Maintenance: Route Maintenance is done by the propagation of route error
message (RERR). Whenever any active node sees or detects the link failure, it
propagates the route error message to its upstream neighbours along the reverse
path till it reaches the source node. To verify the correct operation of the router
links, HELLO messages and acknowledgement messages can be used.
3.10 Classification of the VANET Applications:
The applications of Vehicular Ad hoc Networks (VANET) are classified into three major
groups: 1)comfort oriented applications 2) convenience-oriented applications and 3)safety
oriented applications [11]. Safety oriented related applications look for the increasing
security of passengers by exchanging relevant information through vehicle-to-
infrastructure and vehicle-to-vehicle. And comfort and convenience applications improve
passenger’s comfort and traffic efficiency.
3.10.1 Safety-Oriented Applications: These types of applications help the driver to avoid
potential dangers through the exchange of information among vehicles. They are the
important applications because they serve to avoid accidents [11].
Figure 3.17: Safety Applications provided by VANET
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They can take control of the vehicle in case of hazardous conditions, as in the case of the
automatic braking and only send warning/emergency messages to drivers. Some safety
oriented application shown in Table 3.1[11].
Table 3.1: Examples of Safety-Oriented Applications
Name Description
Intersection violation warning Intersection violation warns drivers when they are
going to pass over a traffic red light .
On-coming traffic warning It assists the driver during over taking manoeuvres
Electronic brake warning It alerts to the driver that a preceding vehicle has
performed a emergent braking.
Vehicle stability warning It reports drivers that they should activate the vehicle
stability control system.
Post-crash notification A vehicle involved in an accident sends warning
/emergency messages in broadcast to approaching
vehicles.
Traffic signal violation warning A roadside unit (RSU) sends messages in broadcast to
warn drivers of potential violations of traffic signals.
Lane change warning It assists drivers to perform a safe lane change
3.10.2 Convenience-Oriented Applications: These types of applications improve the
efficiency of the roads and to save drivers time . Various Convenience oriented
application shown in Table 3.2[11]. Some examples of Convenience Oriented
Applications in VANET as shown in table 3.2.
Table 3.2: Examples of Convenience-Oriented Applications
Name Description
Intersection management Vehicle to vehicle and Vehicle to RSU
communications allow a better intersections
management
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Limited access and detour warning A roadside unit (RSU) sends information in
broadcast about limited access network areas or
detours.
Electronic toll collection A vehicle founds unicast communication with a
toll gate road side unit( RSU) and pays the toll
without stopping vehicle.
Parking availability notification A vehicle asks to a roadside unit (RSU) for a list of
available parking areas or parking spaces, and the
roadside unit (RSU) sends the list to the vehicle.
Congested road notification A vehicle in a congested road forwards
information to other vehicles.
Figure 3.18: Convenience Oriented Application Provided by VANET
3.10.3 Commercial-Oriented Applications: These types of applications serve to make
the travelling more comfortable for example, by means of the internet connection. Few
Commercial oriented application shown in Table 3.3[11].
Table 3.3: Examples of Commercial-Oriented Applications
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Name Description
Remote diagnosis The vehicle driver can start a wireless connection with the
dealer to upload the vehicle diagnostics information to
identify the possible problems.
Media or map download A vehicle can start a wireless connection with the hot-spot
network and home network to download multimedia
contents and location map.
Service announcement Restaurants and other businesses can use a roadside
unit(RSU) to send promotional messages to the drivers
that are in their communication range.