The document discusses using a genetic algorithm to solve the complex problem of university course scheduling. It describes the problem which involves assigning courses, professors, classrooms and time slots while satisfying various hard and soft constraints. A phased approach is proposed which first assigns professors to subjects, then labs to courses, followed by assigning lectures to time slots and labs/tutorials to days and time slots. The genetic algorithm representation and fitness function are defined based on the scheduling problem. The approach is demonstrated on the course scheduling problem at Christ University and is able to generate a timetable that satisfies the hard constraints, though some soft constraints remain unsatisfied.
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.
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.
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.
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
This document provides an introduction to genetic algorithms (GAs) including:
- GAs are inspired by Darwin's theory of evolution and use techniques like inheritance, mutation, selection, and crossover to find solutions to optimization problems.
- The document discusses key GA components like populations of individuals, fitness functions, selection, crossover, and mutation.
- Examples of GA applications to energy management problems are presented including categorizing appliances, pricing schemes, and parametrizing a GA for scheduling home appliances.
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.
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.
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.
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.
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
This document provides an introduction to genetic algorithms (GAs) including:
- GAs are inspired by Darwin's theory of evolution and use techniques like inheritance, mutation, selection, and crossover to find solutions to optimization problems.
- The document discusses key GA components like populations of individuals, fitness functions, selection, crossover, and mutation.
- Examples of GA applications to energy management problems are presented including categorizing appliances, pricing schemes, and parametrizing a GA for scheduling home appliances.
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 discusses fuzzy genetic algorithms (FGAs), which combine fuzzy logic and genetic algorithms. It provides definitions of fuzzy logic and genetic algorithms. Fuzzy logic handles imprecise variables between 0 and 1, while genetic algorithms use techniques like selection, crossover and mutation to evolve solutions. The document notes that FGAs use fuzzy logic techniques to improve genetic algorithm behavior and components. It describes different FGA approaches and lists application sectors like engineering and economics.
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.
Genetic programming is an evolutionary algorithm that uses principles of natural selection and genetics to automatically generate computer programs to solve problems. It works by generating an initial population of random programs, evaluating their performance on the task, and breeding new programs through genetic operations like crossover and mutation. The fittest programs are selected to pass their traits to the next generation, while less fit programs are removed. This process is repeated until an optimal program is found. Genetic programming represents programs as syntax trees and evolves these trees to find solutions without requiring the programmer to specify the form or structure of the solution.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
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 (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.
The document compares five evolutionary optimization algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, ant colony systems, and shuffled frog leaping. It provides a brief description of each algorithm, including how they are inspired by natural processes and behaviors. It also includes pseudocode to facilitate implementing each algorithm. The document then presents benchmark comparisons of the five algorithms on continuous and discrete optimization problems in terms of processing time, convergence speed, and solution quality. It discusses the performance of evolutionary algorithms and provides guidelines for determining the best parameters for each.
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.
Genetic algorithms are a class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They work by maintaining a population of potential solutions and applying genetic operators of selection, crossover and mutation to generate new populations in search of an optimal solution. A genetic algorithm begins with a randomly generated population that is evaluated and selected using a fitness function. Selected solutions then reproduce through crossover and mutation to create a new population, and the process repeats until a termination condition is reached.
This document provides an introduction to genetic algorithms. It discusses how genetic algorithms are inspired by Darwinian evolution and natural selection. The key components of a genetic algorithm are described as follows:
1) A genetic algorithm starts with a population of random solutions called chromosomes.
2) Genetic operators such as selection, crossover and mutation are applied to generate a new population of solutions. Selection favors the fittest solutions based on a fitness function. Crossover combines parts of different solutions, while mutation introduces random changes.
3) The algorithm iterates, applying the genetic operators to successive generations, until an optimal solution emerges or a stopping criteria is met. Genetic algorithms can find multiple optimal solutions and do not require additional information beyond the fitness
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.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
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.
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...ijseajournal
This document summarizes a research paper that proposes a new approach called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) for generating structural test cases using a genetic algorithm. DSGA aims to generate a complete test suite with a single run by finding test cases that cover different areas of the program structure. It begins by finding test cases that cover some areas, then excludes those areas to search for test cases covering other uncovered areas, similar to how humans generate structural test cases. The paper evaluates DSGA on the Triangle Classification algorithm and finds it able to generate a complete test suite without limitations of other genetic algorithm approaches for structural test case generation.
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.
This document discusses using evolutionary techniques like genetic algorithms and particle swarm optimization to reduce the order of large-scale linear systems. Specifically, it examines using GA and PSO to find stable reduced order models by minimizing the integral squared error between the original and reduced system responses. Both techniques guarantee stability if the original system is stable. The paper provides background on model order reduction techniques and describes how GA and PSO are applied, including representing systems as chromosomes and using selection, crossover, and mutation operators. Results from a numerical example are compared to a conventional method.
1. Genetic algorithms are stochastic optimization algorithms inspired by natural evolution that use operations like selection, crossover and mutation to evolve solutions to problems.
2. They work on a population of potential solutions, evaluating their fitness to survive and reproduce, with reproduction combining traits of parents to form new potential solutions over generations.
3. Genetic algorithms find wide applications in function optimization, multi-objective optimization, scheduling, image processing, engineering design and other domains requiring search and optimization.
The document discusses fault management in wireless sensor networks. It proposes a failure detection scheme for event-driven wireless sensor networks using the MANNA management architecture. The scheme aims to provide self-configuration, self-diagnosis, and self-healing capabilities to detect failures without incurring high overhead costs. The performance of the management solution is evaluated through simulations of a temperature monitoring application in event-driven wireless sensor networks under different failure scenarios.
Este documento presenta información sobre varios temas relacionados con la Web 2.0. Incluye definiciones breves de Wi-Fi, wikis, redes LAN y WAN, comunicaciones inalámbricas, libros digitales, TIC y la propia Web 2.0, describiendo sus características principales en 1 o 2 oraciones cada uno.
This document discusses fuzzy genetic algorithms (FGAs), which combine fuzzy logic and genetic algorithms. It provides definitions of fuzzy logic and genetic algorithms. Fuzzy logic handles imprecise variables between 0 and 1, while genetic algorithms use techniques like selection, crossover and mutation to evolve solutions. The document notes that FGAs use fuzzy logic techniques to improve genetic algorithm behavior and components. It describes different FGA approaches and lists application sectors like engineering and economics.
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.
Genetic programming is an evolutionary algorithm that uses principles of natural selection and genetics to automatically generate computer programs to solve problems. It works by generating an initial population of random programs, evaluating their performance on the task, and breeding new programs through genetic operations like crossover and mutation. The fittest programs are selected to pass their traits to the next generation, while less fit programs are removed. This process is repeated until an optimal program is found. Genetic programming represents programs as syntax trees and evolves these trees to find solutions without requiring the programmer to specify the form or structure of the solution.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
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 (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.
The document compares five evolutionary optimization algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, ant colony systems, and shuffled frog leaping. It provides a brief description of each algorithm, including how they are inspired by natural processes and behaviors. It also includes pseudocode to facilitate implementing each algorithm. The document then presents benchmark comparisons of the five algorithms on continuous and discrete optimization problems in terms of processing time, convergence speed, and solution quality. It discusses the performance of evolutionary algorithms and provides guidelines for determining the best parameters for each.
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.
Genetic algorithms are a class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. They work by maintaining a population of potential solutions and applying genetic operators of selection, crossover and mutation to generate new populations in search of an optimal solution. A genetic algorithm begins with a randomly generated population that is evaluated and selected using a fitness function. Selected solutions then reproduce through crossover and mutation to create a new population, and the process repeats until a termination condition is reached.
This document provides an introduction to genetic algorithms. It discusses how genetic algorithms are inspired by Darwinian evolution and natural selection. The key components of a genetic algorithm are described as follows:
1) A genetic algorithm starts with a population of random solutions called chromosomes.
2) Genetic operators such as selection, crossover and mutation are applied to generate a new population of solutions. Selection favors the fittest solutions based on a fitness function. Crossover combines parts of different solutions, while mutation introduces random changes.
3) The algorithm iterates, applying the genetic operators to successive generations, until an optimal solution emerges or a stopping criteria is met. Genetic algorithms can find multiple optimal solutions and do not require additional information beyond the fitness
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.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
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.
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...ijseajournal
This document summarizes a research paper that proposes a new approach called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) for generating structural test cases using a genetic algorithm. DSGA aims to generate a complete test suite with a single run by finding test cases that cover different areas of the program structure. It begins by finding test cases that cover some areas, then excludes those areas to search for test cases covering other uncovered areas, similar to how humans generate structural test cases. The paper evaluates DSGA on the Triangle Classification algorithm and finds it able to generate a complete test suite without limitations of other genetic algorithm approaches for structural test case generation.
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.
This document discusses using evolutionary techniques like genetic algorithms and particle swarm optimization to reduce the order of large-scale linear systems. Specifically, it examines using GA and PSO to find stable reduced order models by minimizing the integral squared error between the original and reduced system responses. Both techniques guarantee stability if the original system is stable. The paper provides background on model order reduction techniques and describes how GA and PSO are applied, including representing systems as chromosomes and using selection, crossover, and mutation operators. Results from a numerical example are compared to a conventional method.
1. Genetic algorithms are stochastic optimization algorithms inspired by natural evolution that use operations like selection, crossover and mutation to evolve solutions to problems.
2. They work on a population of potential solutions, evaluating their fitness to survive and reproduce, with reproduction combining traits of parents to form new potential solutions over generations.
3. Genetic algorithms find wide applications in function optimization, multi-objective optimization, scheduling, image processing, engineering design and other domains requiring search and optimization.
The document discusses fault management in wireless sensor networks. It proposes a failure detection scheme for event-driven wireless sensor networks using the MANNA management architecture. The scheme aims to provide self-configuration, self-diagnosis, and self-healing capabilities to detect failures without incurring high overhead costs. The performance of the management solution is evaluated through simulations of a temperature monitoring application in event-driven wireless sensor networks under different failure scenarios.
Este documento presenta información sobre varios temas relacionados con la Web 2.0. Incluye definiciones breves de Wi-Fi, wikis, redes LAN y WAN, comunicaciones inalámbricas, libros digitales, TIC y la propia Web 2.0, describiendo sus características principales en 1 o 2 oraciones cada uno.
This document discusses restorative justice and innovation in organizational structures. It explores how prevailing management beliefs were created by punitive systems and proposes innovating how organizations work to be more congruent with restorative justice principles. The document suggests challenging assumptions, exploring possibilities in different fields, and sharing ideas through various mediums to drive innovation.
This document discusses how research in instructional design and nursing can be combined to improve nursing education. It explains that instructional design research focuses on the learning process and effective instructional strategies, while nursing research examines settings, processes, and patient types. Through collaboration between the two fields and the use of simulations, evidence-based nursing practices can be developed and new instructional strategies for nursing education identified. The goal is to apply the findings of instructional design and nursing research to improve nursing procedure education.
This document appears to be a class roster listing 12 students' names from Class 1/A of Nejat Sabuncu Primary School. The list includes the names Yaşarcan, Nihanur, Fatmanur, Dilara, Emre, Pelin, Berkcan, Zehra, Berfinnaz, Anil, Talha, and Mustafa.
La talasemia es una enfermedad hereditaria caracterizada por la producción anormal de hemoglobina, lo que causa anemia. Existen varios tipos dependiendo de si hay deficiencia en las cadenas alfa o beta de la hemoglobina. Los síntomas incluyen fatiga, palidez y agrandamiento del bazo. El diagnóstico se realiza mediante análisis de sangre y se trata principalmente con transfusiones de sangre.
The document discusses managing order batching issues in supply chain management using a multi-agent system. It first provides background on multi-agent systems and their advantages over centralized systems, such as being able to solve problems that are too large or complex for a single agent. It then discusses how a multi-agent system can be used to handle the order batching problem in supply chain management, which is a major cause of the bullwhip effect that negatively impacts supply chain performance. The proposed system uses intelligent agents to maintain information related to order batching issues and make decisions to manage order batching.
Mental Health First Aid England aims to improve the nation's mental health literacy through training one in ten adults. Studies show only 20% of the population has high mental well-being and mental illness accounts for a large disease burden. MHFA training significantly improves confidence and knowledge in supporting others with mental health problems. Evaluations found over 95% of trainees rated the course structure, content, and overall experience positively. MHFA England works to advance public health priorities by focusing on prevention, early intervention, and recovery for conditions like anxiety, depression and more.
The document discusses using the Nintendo Wii video game console in an English as a Second Language (ESL) classroom. It notes that the Wii is affordable, with the console costing $199.99 and remotes $30 each. Many students likely already own or are familiar with the Wii. It also functions as a computer to access educational online games and the internet. Using Wii games can reinforce language learning and be used for spelling bees or memory games. The Wii allows active learning and games exist that help with vocabulary and language skills. However, supervision is needed to keep students focused on the educational content.
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.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
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 article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
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.
List the problems that can be efficiently solved by Evolutionary P.pdfinfomalad
List the problems that can be efficiently solved by Evolutionary Programming?
List the problems that can be efficiently solved by Evolutionary Programming?
Solution
Evolutionary Programming is a Global Optimization algorithm and is an instance of an
Evolutionary Algorithm from the field of Evolutionary Computation. The approach is a sibling
of other Evolutionary Algorithms such as the Genetic Algorithm, and Learning Classifier
Systems. It is sometimes confused with Genetic Programming given the similarity in name, and
more recently it shows a strong functional similarity to Evolution Strategies.
Inspiration
Evolutionary Programming is inspired by the theory of evolution by means of natural selection.
Specifically, the technique is inspired by macro-level or the species-level process of evolution
(phenotype, hereditary, variation) and is not concerned with the genetic mechanisms of evolution
(genome, chromosomes, genes, alleles).
Metaphor
A population of a species reproduce, creating progeny with small phenotypical variation. The
progeny and the parents compete based on their suitability to the environment, where the
generally more fit members constitute the subsequent generation and are provided with the
opportunity to reproduce themselves. This process repeats, improving the adaptive fit between
the species and the environment.
Strategy
The objective of the Evolutionary Programming algorithm is to maximize the suitability of a
collection of candidate solutions in the context of an objective function from the domain. This
objective is pursued by using an adaptive model with surrogates for the processes of evolution,
specifically hereditary (reproduction with variation) under competition. The representation used
for candidate solutions is directly assessable by a cost or objective function from the domain.
Problems for evolutionary algorithms
Evolutionary programming are useful for problems for which no mechanistic method is available
– or when unreasonable assumptions need to be made. Problems for which you cannot calculate
or derive a solution. Problems for which you have to find a solution.
Aspects of problems that lend them to Evolutionary Programming solution include:
Assignment of individuals into groups, wherever simple ranking and truncation will not work.
This usually involves interactions, whereby whether an individual should be in a group depends
on what other individuals will be in that group. Example: developing multiplex groupings for
genotyping.
Problems where thresholds are involved, for example when the value of a solution depends on
whether certain thresholds have been passed, or the fate of an individual depends on passing one
or more thresholds. Example: Supply chain optimizing to target multiple product end-points
and/or turnoff dates.
Combinatorially tedious problems, where many combinations of components can exist. Example:
The setting up of animal matings. Below are the few applications:
Learning classifier systems, or .
S.N.Sivanandam & S.N. Deepa - Introduction to Genetic Algorithms 2008 ISBN 35...edwinray3
This document provides an introduction to genetic algorithms. It discusses the historical development of evolutionary computation, including genetic algorithms, genetic programming, evolutionary strategies, and evolutionary programming. The key features of evolutionary computation are described, such as particulate genes and population genetics, the adaptive code book, and the genotype/phenotype dichotomy. Advantages of evolutionary computation are highlighted, including conceptual simplicity, broad applicability, hybridization with other methods, parallelism, robustness to dynamic changes, and solving problems with no known solutions. The document concludes with a discussion of applications of evolutionary computation.
Preparation of courses at every university is done by hand. This method has limitations that often cause collisions schedule. In lectures and lab scheduling frequent collision against the faculty member teaching schedule, collisions on the class schedule and student, college collision course with lab time, the allocation of the use of the rooms were not optimal. Heuristic method of genetic algorithm based on the mechanism of natural selection; it is a process of biological evolution. Genetic algorithms are used to obtain optimal schedule that consists of the initialization process of the population, fitness evaluation, selection, crossover, and mutation. Data used include the teaching of data, the data subjects, the room data and time data retrieved from the database of the Faculty of Computer Science, Universitas Pembangunan Panca Budi. The data in advance through the stages of the process of genetic algorithms to get optimal results The results of this study in the form of a schedule of courses has been optimized so that no error occurred and gaps.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
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.
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.
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.
In this Machine Learning tutorial, we will cover the top Neural Network Algorithms. These algorithms are used to train the Artificial Neural Network. This blog provides you a deep learning of the Gradient Descent, Evolutionary Algorithms, and Genetic Algorithm in Neural Network.
This document discusses advanced optimization techniques used to solve large-scale problems that traditional techniques cannot handle effectively. It introduces several population-based metaheuristic algorithms inspired by natural phenomena, including genetic algorithms, artificial immune algorithms, and differential evolution. Genetic algorithms use operations like selection, crossover and mutation to evolve solutions over generations. Artificial immune algorithms are based on clonal selection to amplify high-affinity antibodies. Differential evolution generates trial vectors through mutation and crossover of randomly selected target vectors.
This document describes a new approach that combines the analytical hierarchy process (AHP) and genetic algorithm (GA) to solve the timetable problem in schools. AHP is used to rank teachers based on criteria and assign a score to each teacher. GA is then used to generate timetable schedules that aim to satisfy teacher preferences, with the fitness function considering teacher scores from AHP. The approach was tested on a simple example of scheduling classes and teachers across days and time slots. By incorporating teacher rankings and preferences, the AHP/GA approach aims to produce timetables that satisfy teachers more than existing manual or automated methods.
Traveling Salesman Problem (TSP) is a kind of NPHard problem which cant be solved in polynomial time for
asymptotically large values of n. In this paper a balanced combination of Genetic algorithm and Simulated Annealing is used. To
improve the performance of finding optimal solution from huge
search space, we have incorporated the use of tournament and
rank as selection operator. And Inver-over operator Mechanism
for crossover and mutation . To illustrate it more clearly an
implementation in C++ (4.9.9.2) has been done.
Index Terms—Genetic Algorithm (GA) , Simulated Annealing
(SA) , Inver-over operator , Lin-Kernighan algorithm , selection
operator , crossover operator , mutation operator.
A MULTI-POPULATION BASED FROG-MEMETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMacijjournal
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manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this
paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA,
considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub
populations and then solve the problem using a Memetic algorithm. The proposed approach have been
compared with other algorithms for the Job Shop Scheduling and evaluated with satisfactory results on a
set of the JSSP instances derived from classical Job Shop Scheduling benchmarks. We have solved 20
benchmark problems from Lawrence’s datasets and compared the results obtained with the results of the
algorithms established in the literature. The experimental results show that JSFMA could gain the best
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Ar03402580261
1. International Journal of Computational Engineering Research||Vol, 03||Issue, 4||
www.ijceronline.com ||April||2013|| Page 258
A Phased Approach to Solve the University Course Scheduling
System
Rohini V,
Department of Computer Science, Christ University, Bangalore
I. INTRODUCTION
Course scheduling in a university is a complex manual work done twice in a year. The problem
involves fixing the time slots for the subjects, allocating the resources (lab, class rooms, projectors), allocating
the staff satisfying the hard constraints and minimize the cost of a set of soft constraints. The constraints will
not be common for all universities. It will be practised according to the constraints of the departments.
II. GENETIC ALGORITHM
In 1975, Holland developed the idea of genetic algorithm idea in his book “Adaptation in natural and
artificial systems”[2]. He described how to apply the principles of natural evolution to optimization problems
and built the first Genetic Algorithms. Holland’s theory has been further developed and now Genetic
Algorithms (GAs) stand up as a powerful tool for solving search and optimization.An algorithm is a series of
steps for solving a problem. A genetic algorithm is a problem solving method that uses genetics as its model of
problem solving. It’s a search technique to find approximate solutions to optimization and search
problems[4].Basically, an optimization problem looks really simple. One knows the form of all possible
solutions corresponding to a specific question. The set of all the solutions that meet this form constitute the
search space. The problem consists in finding out the solution that fits the best, i.e. the one with the most
payoffs, from all the possible solutions. If it’s possible to quickly enumerate all the solutions, the problem does
not raise much difficulty. Genetic Algorithms work in a similar way, adapting the simple genetics to algorithmic
mechanisms.GA handles a population of possible solutions. Each solution is represented through a chromosome,
which is just an abstract representation. Coding all the possible solutions into a chromosome is the first part, but
certainly not the most straightforward one of a Genetic Algorithm. A set of reproduction operators has to be
determined, too. Reproduction operators are applied directly on the chromosomes and are used to perform
mutations and recombinations over solutions of the problem. Appropriate representation and reproduction
operators are really something determinant, as the behavior of the GA is extremely dependant on it.
Frequently, it can be extremely difficult to find a representation, which respects the structure of the
search space and reproduction operators, which are coherent and relevant according to the properties of the
problems.Selection is supposed to be able to compare each individual in the population.Selection is done by
using a fitness function. Each chromosome has an associated value corresponding to the fitness of the solution it
represents. The fitness should correspond to an evaluation of how good the candidate solution is. The optimal
solution is the one, which maximizes the fitness function. Genetic Algorithms deal with the problems that
maximize the fitness function. But, if the problem consists in minimizing a cost function, the adaptation is quite
easy. Either the cost function can be transformed into a fitness function, for example by inverting it; or the
selection can be adapted in such way that they consider individuals with low evaluation functions as better.
Abstract:
Scheduling is an important activity of our life. Course scheduling is a complicated task faced by
every university. This paper presents a phased approach to solve the constraint satisfaction problem of
scheduling the resources. Here the idea is to use a genetic algorithm which is a good solution for the NP
hard problems like scheduling. The paper starts by defining the problem of scheduling the courses in
Christ University, Department of computer science and defining the various complicated constraints
available. Then the problem model is explained with the set of resources like professors, rooms, labs,
student etc. Finally the paper describes the different phases used to get the final well defined
schedule[1].
Keywords: Constraints, Course scheduling, Genetic algorithm, NP hard problem, Phased, Hard
constraints, Soft constraints
2. A Phased Approach To Solve The University…
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Once the reproduction and the fitness function have been properly defined, a Genetic Algorithm is evolved
according to the same basic structure. It starts by generating an initial population of chromosomes. This first
population must offer a wide diversity of genetic materials. The gene pool should be as large as possible so that
any solution of the search space can be engendered. Generally, the initial population is generated randomly.
Then, the genetic algorithm loops over an iteration process to make the population evolve. Each iteration
consists of the following steps:
SELECTION: The first step consists in selecting individuals for reproduction.
This selection is done randomly with a probability depending on the relative fitness of the
individuals so that best ones are often chosen for reproduction than poor ones.
REPRODUCTION: In the second step, offspring are bred by the selected individuals.
For generating new chromosomes, the algorithm can use both recombination and mutation.
EVALUATION: Then the fitness of the new chromosomes is evaluated.
REPLACEMENT: During the last step, individuals from the old population are killed and replaced by the
new ones.
The algorithm is stopped when the population converges toward the optimal solution.
The basic genetic algorithm is as follows:
[start] Genetic random population of n chromosomes (suitable solutions for the problem)
[Fitness] Evaluate the fitness f(x) of each chromosome x in the population
New population] Create a new population by repeating following steps until the New population is
complete
[selection] select two parent chromosomes from a population according to their fitness ( the better fitness,
the bigger chance to get selected).
[crossover] With a crossover probability, cross over the parents to form new offspring ( children). If no
crossover was performed, offspring is the exact copy of parents.
[Mutation] With a mutation probability, mutate new offspring at each locus (position in chromosome)
[Accepting] Place new offspring in the new population.
2.1 A Simple Genetic Algorithm
[Replace] Use new generated population for a further sum of the algorithm.
[Test] If the end condition is satisfied, stop, and return the best solution in current population.
[Loop] Go to step2 for fitness evaluation.
The Genetic algorithm process is discussed through the GA cycle in Fig. 1.
Fig.1 Genetic algorithm cycle
Reproduction is the process by which the genetic material in two or more parent is combined to obtain one or
more offspring. In fitness evaluation step, the individual’s quality is assessed. Mutation is performed to one
individual to produce a new version of it where some of the original genetic material has been randomly
changed. Selection process helps to decide which individuals are to be used for reproduction and mutation in
order to produce new search points.
The flowchart showing the process of GA is as shown in Fig. 2.
3. A Phased Approach To Solve The University…
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Fig.2 Genetic algorithm flowchart
Before implementing GA, it is important to understand few guidelines for designing a general search algorithm
i.e. a global optimization algorithm based on the properties of the fitness landscape and the most common
optimization method types:
[1] determinism:A purely deterministic search may have an extremely high variance in solution quality because
it may soon get stuck in worst case situations from which it is incapable to escape because of its
determinism. This can be avoided, but it is a well-known fact that the observation of the worst-case
situation is not guaranteed to be possible in general.
[2] nondeterminism: A stochastic search method usually does not suffer from the above potential worst case
”wolf trap” phenomenon. It is therefore likely that a search method should be stochastic, but it may well
contain a substantial portion of determinism, however. In principle it is enough to have as much
nondeterminism as to be able to avoid the worst-case wolf traps.
III. EMPIRICAL STUDY
In this section, a real world university course scheduling system is represented and is solved by the
genetic algorithm described above.The Department of Computer Science, Christ University, Bangalore is a big
department consisting of various courses in computer science as BCA, B.Sc., MCA, M.Sc. and consists of thirty
professors. Scheduling the subjects to the various course in the allotted time slot and resources is a complex
hard problem. The job involves of scheduling the various subjects to the course , various staff and resources
adhering to the constraints stated below. The problem consists of the following entities:
Days, Timeslots, and Periods.:We are given a number of teaching days in the week (typically 6). Each day is
split in a fixed number of timeslots of 6 ( 9 am to 4 pm, except 1 – 2 pm for lunch) and Saturdays only 4 hours
(9 am – 1 pm). A period is a pair composed by a day and a timeslot. The total number of scheduling periods is
the product of the days times the day timeslots.
Courses and professors: Each course consists of a fixed number of lectures to be scheduled in distinct periods;
it is attended by given number of students, and is taught by a teacher. For each course there is a minimum
number of days that the lectures of the course should be spread in, moreover there are some periods in which the
course cannot be scheduled.
Rooms.:Each room has a capacity, expressed in terms of number of available seats. All rooms are equally
suitable for all courses (if large enough). Rooms will have the resources like board(white / black), Projector etc.
The timetables are feasible if and only if the following hard constraints are satisfied:
4. A Phased Approach To Solve The University…
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No instructor teaches more than one course at a given time.
Number of courses taught during any time slot should not exceed the maximum number of classrooms and labs
available at that specified time.
Restrictions on the starting time, ending time, lunch time.
Lectures, seminars and labs of each group of students should not overlap.
Practical courses should run on two continuous time slots.
Class timings is 9 -4 with a break of lunch from 1-2 pm.
Subjects of other department should be accommodated in their possible time slot.
While two Elective of one course is happening, one more class should be in the lab.
The following soft constraints should be satisfied if possible:
There should be a gap of at least one hour in the schedule between courses taught by the same instructor.
Course should be spread over evenly throughout the week (Monday to Saturday).
All lectures of a particular course should be assigned to the same room.
Students can attend only one lecture at a time.
All first hour (9-10 am) should be allocated to different subject/faculty.
The problem is solved in a phased approach as follows :[5]
Phase 1: Allot the subjects to the professors.
Phase II : Allot the labs to the courses..
Phase III: Assign lectures to time slots.
Phase IV: Assigns labs and tutorials to days and available time slots in the days.
The flow of data as allocation of subjects of the various courses to the professors are done and given to the
committee. The scheduling committee is responsible for allotting the time slots, resources to the various courses
satisfying the above mentioned constraints. According to the genetic algorithm method described in the paper
and the relationship of those events and resources, a course scheduling system is developed. Finally, a solution
which has the best fitness is obtained. Through analysis of the time table prepared, the constraints are satisfied
and some soft constraints are not able to satisfy. It is demonstrated that the method in this problem is feasible
and widely accepted.
IV. CONCLUSION
University course scheduling system is a NP hard problem[1].In this paper, we have discussed about
the real life case study of how the timetabling is done with the constraints and the goal of optimization etc.
Genetic algorithm, which is a commonly used evolutionary algorithm is applied to solve the problem described.
During the process, genetic representation and a fitness function are defined according to the problem.
REFERENCES
[1] Bratković, Z., Herman, T., Omrčen, V., Čupić, M., Jakobović, D.: University course timetabling with genetic algorithm: A
laboratory exercises case study. In: Cotta, C., Cowling, P. (eds.) EvoCOP 2009. LNCS, vol. 5482, pp. 240–251. Springer,
Heidelberg (2009)
[2] J.H. Holland , Adaptation in Natural and Artificial systems, Cambridge, The MIT press, 1992
[3] D.Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning”, Addison Wesley, 1989
[4] S.N.Sivanandam and S.N.Deepa , Introduction to Genetic Algorithms, Springer, 2007
[5] Minhaz Fahim Zibran, A Multi-phase Approach to University Course Timetabling, University of Lethbridge (Canada), 2007