The document provides an overview of genetic algorithms, including their inspiration from evolution, the basic algorithm, why they work, strengths and weaknesses, and applications. It summarizes the encoding, selection, crossover, and mutation steps of the basic genetic algorithm. It also gives examples of genetic algorithms applied to the traveling salesman problem (TSP), including encoding solutions and crossover/mutation operators.
The document describes genetic algorithms, which are inspired by biological evolution. It discusses how genetic algorithms work by starting with a random population that undergoes selection, crossover, and mutation to generate new solutions. The population evolves over multiple generations as higher-fitness solutions are more likely to be selected for reproduction and combination with other solutions. This evolutionary process can help search large problem spaces to find optimal or near-optimal solutions.
advance operators.
explain about the diploid , dominance, and partial match crossover and the order crossover
Technologies
Multi objective optimization , knowledge base technologies hibrid, parallel computing
The document discusses using a genetic algorithm to optimize the mass design of a single-stage helical gear unit. The objective is to minimize the total mass of the gear unit, which is calculated based on the volumes and densities of its various components. The design must satisfy 38 constraints related to gear ratios, stresses, clearances, manufacturability, and component life. A genetic algorithm is applied to search for the design variable values that minimize mass subject to all constraints.
This document discusses genetic algorithms. It introduces genetic algorithms as global search heuristics inspired by evolutionary biology concepts like inheritance, mutation, and crossover. It describes the key genetic algorithm operators of reproduction, crossover, and mutation. Reproduction selects above-average solutions for breeding, crossover combines parts of parent solutions, and mutation randomly changes parts of a solution. The document outlines the genetic algorithm process of initializing a random population, evaluating via a fitness function, selecting parents, applying operators, and iterating until termination criteria is met. Genetic algorithms are advantageous for complex problems as they use populations to search in parallel and only require objective function values rather than derivatives.
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 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.
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.
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.
The document describes genetic algorithms, which are inspired by biological evolution. It discusses how genetic algorithms work by starting with a random population that undergoes selection, crossover, and mutation to generate new solutions. The population evolves over multiple generations as higher-fitness solutions are more likely to be selected for reproduction and combination with other solutions. This evolutionary process can help search large problem spaces to find optimal or near-optimal solutions.
advance operators.
explain about the diploid , dominance, and partial match crossover and the order crossover
Technologies
Multi objective optimization , knowledge base technologies hibrid, parallel computing
The document discusses using a genetic algorithm to optimize the mass design of a single-stage helical gear unit. The objective is to minimize the total mass of the gear unit, which is calculated based on the volumes and densities of its various components. The design must satisfy 38 constraints related to gear ratios, stresses, clearances, manufacturability, and component life. A genetic algorithm is applied to search for the design variable values that minimize mass subject to all constraints.
This document discusses genetic algorithms. It introduces genetic algorithms as global search heuristics inspired by evolutionary biology concepts like inheritance, mutation, and crossover. It describes the key genetic algorithm operators of reproduction, crossover, and mutation. Reproduction selects above-average solutions for breeding, crossover combines parts of parent solutions, and mutation randomly changes parts of a solution. The document outlines the genetic algorithm process of initializing a random population, evaluating via a fitness function, selecting parents, applying operators, and iterating until termination criteria is met. Genetic algorithms are advantageous for complex problems as they use populations to search in parallel and only require objective function values rather than derivatives.
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 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.
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.
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.
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.
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.
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 discusses genetic algorithms, which are adaptive heuristic search algorithms based on natural selection and genetics. Genetic algorithms generate potential solutions and evaluate their fitness to determine which solutions are best suited for evolving toward an answer. Potential solutions are encoded as binary bit strings called chromosomes. The genetic algorithm operates by initializing a random population, determining fitness, selecting parents for reproduction, performing crossover and mutation on offspring, and evaluating the new population in an iterative process until a termination criteria is met.
This document provides an overview of genetic algorithms (GAs). It describes Holland's simple genetic algorithm (SGA) model including representation, selection, crossover and mutation operators. Real-valued and permutation representations are discussed along with associated operators. Alternative population models and selection mechanisms are also summarized.
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.
Performance of genetic algorithm is flexible enough to make it applicable to a wide range of problems, such as the problem of placing N queens on N by N chessboard in order that no two queens can attack each other which is known as ‘n-Queens problem.
Lack of information about details of the problem made genetic algorithm confused in searching state space of the problem
This course basically deals with the algorithms of genetic part and further deals with how it is formed and what are its techniques and how it can be used in Power
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.
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.
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.
This document discusses genetic algorithms and their use for optimization problems. It begins by defining genetic algorithms as search and optimization techniques based on Darwin's principle of natural selection. It then outlines the components and working of genetic algorithms, including encoding potential solutions as chromosomes, selecting chromosomes based on their fitness, and generating new solutions through crossover and mutation of parents. The document provides an example problem of using genetic algorithms to generate mathematical expressions that equal a target value.
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.
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.
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.
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 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.
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.
The document discusses research on learning to improve the efficiency of machine learning algorithms through speedup learning. It provides three key points:
1) Early work on explanation-based learning for speedup had limited success, but techniques like memoization and clause learning led to major improvements in SAT solvers.
2) More recent approaches use machine learning to build predictive models of problem instances and solver behavior, in order to inform strategies like automatic noise setting and randomized restart policies.
3) Case studies demonstrate these learning-based approaches can outperform traditional techniques and fixed policies by customizing resource allocation and reformulation based on problem structure and solver progress.
The document discusses research on learning to improve the efficiency of machine learning algorithms through speedup learning. It provides three key points:
1) Early work on explanation-based learning for speedup had limited success, but techniques like memoization and clause learning led to major improvements in SAT solvers.
2) More recent approaches use predictive models trained on dynamic features to learn optimal policies for controlling search algorithms, like setting noise levels or restart policies.
3) Open problems remain in developing optimal predictive policies with partial information and approximations, to continue improving search and reasoning performance.
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.
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.
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 discusses genetic algorithms, which are adaptive heuristic search algorithms based on natural selection and genetics. Genetic algorithms generate potential solutions and evaluate their fitness to determine which solutions are best suited for evolving toward an answer. Potential solutions are encoded as binary bit strings called chromosomes. The genetic algorithm operates by initializing a random population, determining fitness, selecting parents for reproduction, performing crossover and mutation on offspring, and evaluating the new population in an iterative process until a termination criteria is met.
This document provides an overview of genetic algorithms (GAs). It describes Holland's simple genetic algorithm (SGA) model including representation, selection, crossover and mutation operators. Real-valued and permutation representations are discussed along with associated operators. Alternative population models and selection mechanisms are also summarized.
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.
Performance of genetic algorithm is flexible enough to make it applicable to a wide range of problems, such as the problem of placing N queens on N by N chessboard in order that no two queens can attack each other which is known as ‘n-Queens problem.
Lack of information about details of the problem made genetic algorithm confused in searching state space of the problem
This course basically deals with the algorithms of genetic part and further deals with how it is formed and what are its techniques and how it can be used in Power
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.
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.
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.
This document discusses genetic algorithms and their use for optimization problems. It begins by defining genetic algorithms as search and optimization techniques based on Darwin's principle of natural selection. It then outlines the components and working of genetic algorithms, including encoding potential solutions as chromosomes, selecting chromosomes based on their fitness, and generating new solutions through crossover and mutation of parents. The document provides an example problem of using genetic algorithms to generate mathematical expressions that equal a target value.
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.
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.
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.
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 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.
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.
The document discusses research on learning to improve the efficiency of machine learning algorithms through speedup learning. It provides three key points:
1) Early work on explanation-based learning for speedup had limited success, but techniques like memoization and clause learning led to major improvements in SAT solvers.
2) More recent approaches use machine learning to build predictive models of problem instances and solver behavior, in order to inform strategies like automatic noise setting and randomized restart policies.
3) Case studies demonstrate these learning-based approaches can outperform traditional techniques and fixed policies by customizing resource allocation and reformulation based on problem structure and solver progress.
The document discusses research on learning to improve the efficiency of machine learning algorithms through speedup learning. It provides three key points:
1) Early work on explanation-based learning for speedup had limited success, but techniques like memoization and clause learning led to major improvements in SAT solvers.
2) More recent approaches use predictive models trained on dynamic features to learn optimal policies for controlling search algorithms, like setting noise levels or restart policies.
3) Open problems remain in developing optimal predictive policies with partial information and approximations, to continue improving search and reasoning performance.
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.
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.
This document describes genetic algorithms and provides an example of how one works. It defines genetic algorithms as evolutionary algorithms that use techniques inspired by evolutionary biology like inheritance, mutation, selection, and crossover. The document then outlines the typical components of a genetic algorithm, including initialization of a random population, fitness evaluation, selection of parents, crossover and mutation to produce offspring, and iteration until a termination condition is met. It concludes by showing pseudocode for a genetic algorithm to solve the onemax problem and output from running the algorithm.
An Improved Iterative Method for Solving General System of Equations via Gene...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
An Improved Iterative Method for Solving General System of Equations via Gene...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
Multi-Domain Diversity Preservation to Mitigate Particle Stagnation and Enab...Weiyang Tong
This paper makes important advancements to a Particle Swarm Optimization (PSO) algorithm that seeks to address the major complex attributes of engineering optimization problems, namely multiple objectives, high nonlinearity, high dimensionality, constraints, and mixed-discrete variables. To introduce these capabilities while keeping PSO competitive with other powerful multi-objective algorithms (e.g., NSGA-II, SPEA, and PAES), it is important to not only preserve population diversity (for mitigating stagnation), but also explicit diversity preservation to facilitate improved converge of (non-convex) Pareto frontiers. A new multi-domain preservation technique is presented in this paper for this purpose. In this technique, an adoptive repulsion is applied to each global leader to slow down the clustering of particles overly popular global leaders, and maintain a desirably even distribution of Pareto optimal solutions. In addition, the global leader selection is now modified to follow a stochastic solution based on a half Gaussian distribution. Specifically, two different population diversity measures are explored: (i) based on the smallest hypercube enclosing the entire population, and (ii) based on the smallest hypercube enclosing the subset of particles following each of the global leaders. Both strategies are investigated using a suite of benchmark problems. The performance of the new PSO algorithm is compared with other algorithms in terms of convergence measure, uniformity measure, and computation time.
Two-Stage Eagle Strategy with Differential EvolutionXin-She Yang
The document describes a two-stage optimization strategy called the Eagle Strategy (ES) that combines global and local search algorithms to improve search efficiency. It evaluates applying ES to differential evolution (DE), a popular evolutionary algorithm. ES first uses randomization like Levy flights for global exploration, then switches to DE for intensive local search around promising solutions. The authors validate ES-DE on test functions, finding it requires only 9.7-24.9% of the function evaluations of pure DE. They also apply it to real-world pressure vessel and gearbox design problems, achieving solutions with 14.9-17.7% fewer function evaluations than pure DE.
This document discusses genetic and evolutionary algorithms. It begins by explaining genetic algorithms, including their origins, how they manage populations of coded solutions, and how they use selection, crossover, and mutation to search for good solutions. It then provides more details on genetic algorithm terminology, features, search processes, and theoretical underpinnings like Holland's schema theorem. The document also discusses how genetic algorithms can be applied to problems with continuous parameters and provides examples of genetic algorithm operators and processes.
This document summarizes evolutionary computation techniques including genetic algorithms and genetic programming. It provides an overview of biological evolution and how evolutionary computation mimics this process to solve problems. Genetic algorithms use chromosomes to represent candidate solutions which are evolved over generations using selection, crossover and mutation operators. Genetic programming uses tree representations to evolve computer programs. The document describes how genetic programming can be used to evolve a program for a wall-following robot. It concludes by discussing applications and advantages/disadvantages of evolutionary computation.
AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENE...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
1. The document discusses an emerging approach to computing called soft computing. Soft computing techniques include neural networks, genetic algorithms, machine learning, probabilistic reasoning, and fuzzy logic.
2. Soft computing aims to develop intelligent machines that can solve real-world problems that are difficult to model mathematically. It exploits tolerance for uncertainty and imprecision similar to human decision making.
3. The document then discusses various soft computing techniques in more detail, including neural networks, genetic algorithms, fuzzy logic, and how they differ from traditional hard computing approaches.
This document discusses radial basis function networks. It begins by introducing the basic structure of RBF networks, which typically involve an input layer, a hidden layer that applies a nonlinear transformation using radial basis functions, and an output layer with a linear transformation. The document then discusses Cover's theorem, which states that pattern classification problems are more likely to be linearly separable when mapped to a higher-dimensional space through a nonlinear transformation. Several key concepts are introduced, including dichotomies, phi-separable functions, and using hidden functions to map patterns to a hidden feature space.
Accelerated life testing plans are designed under multiple objective consideration, with the resulting Pareto optimal solutions classified and reduced using neural network and data envelopement analysis, respectively.
This document describes a new multi-objective evolutionary algorithm called MOSCA2. MOSCA2 improves upon an earlier algorithm called MOSCA by using subpopulations instead of clusters, truncation selection instead of random selection, adding a recombination operator, and adding a separate archive to store non-dominated solutions. The algorithm uses subpopulations, truncation selection, and a deleting procedure to maintain diversity without needing density information or niche methods. It also uses a separate archive that stores and periodically updates non-dominated solutions found, deleting some when the archive becomes full. The algorithm is capable of solving both constrained and unconstrained nonlinear multi-objective optimization problems.
Genetic algorithms are search algorithms inspired by biological evolution that use techniques like mutation, crossover, and selection to evolve solutions to problems. They represent potential solutions as individuals in a population and evolve the population over multiple generations using genetic operators to improve the overall quality of solutions. Genetic programming is a type of genetic algorithm that evolves computer programs to solve problems by genetically breeding populations of computer programs.
Genetic Algorithm for the Traveling Salesman Problem using Sequential Constru...CSCJournals
This paper develops a new crossover operator, Sequential Constructive crossover (SCX), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The sequential constructive crossover operator constructs an offspring from a pair of parents using better edges on the basis of their values that may be present in the parents' structure maintaining the sequence of nodes in the parent chromosomes. The efficiency of the SCX is compared as against some existing crossover operators; namely, edge recombination crossover (ERX) and generalized N-point crossover (GNX) for some benchmark TSPLIB instances. Experimental results show that the new crossover operator is better than the ERX and GNX.
Energy-Based Models with Applications to Speech and Language Processingnxmaosdh232
Energy-based models (EBMs) are an important class of probabilistic models that define a joint probability distribution based on an "energy function". EBMs include undirected graphical models and random fields. The tutorial will cover the basics of EBMs, including learning and inference methods, and applications of EBMs to language modeling, speech recognition, and natural language labeling. It will also discuss upgrading EBMs to handle sequential data and semi-supervised learning tasks.
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.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
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.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
2. Inspiration - Evolution
• Natural Selection:
– “Survival of the Fittest”
– favourable traits become common and
unfavourable traits become uncommon in
successive generations
• Sexual Reproduction:
– Chromosomal crossover and genetic
recombination
– population is genetically variable
– adaptive evolution is facilitated
– unfavourable mutations are eliminated
5. Encoding of Solution Space
Represent solution space by strings of fixed
length over some alphabet
TSP:
ordering of points
Knapsack:
inclusion in knapsack
A D B E C B E D A C
B
A C
E D
0 0 1 0 1 1 0 1 1 0
6. Selection
• Fitness function:
– f(x), x is a chromosome in the solution space
– f(x) may be:
• an well-defined objective function to be optimised
– e.g. TSP and knapsack
• a heuristic
– e.g. N-Queens
• Probability distribution for selection:
• Fitness proportional selection
M
j j
i
i
x
f
x
f
x
X
P
1
)
(
)
(
7. Operators-Crossover and
Mutation
• Crossover:
– Applied with high probability
– Position for crossover on the two parent chromosomes randomly
selected
– Offspring share characteristics of well-performing parents
– Combinations of well-performing characteristics generated
• Mutation:
– Applied with low probability
– Bit for mutation randomly selected
– New characteristics introduced into the population
– Prevents algorithm from getting trapped into a local optimum
8. The Basic Algorithm
1. Fix population size M
2. Randomly generate M strings in the solution space
3. Observe the fitness of each chromosome
4. Repeat:
1. Select two fittest strings to reproduce
2. Apply crossover with high probability to produce offspring
3. Apply mutation to parent or offspring with low probability
4. Observe the fitness of each new string
5. Replace weakest strings of the population with the
offspring
until
i. fixed number of iterations completed, OR
ii. average/best fitness above a threshold, OR
iii. average/best fitness value unchanged for a fixed number of
consecutive iterations
9. Example
• Problem specification:
– string of length 4
– two 0’s and two 1’s
– 0’s to the right of the 1’s
• Solution space:
• Fitness function (heuristic):
– f(x) = number of bits that match the ones in the solution
• Initialization (M = 4):
0 0 1 1
4
1
,
0
1 0 0 0 1
)
(
A
f
0 1 0 0 1
)
(
B
f
0 1 0 1 2
)
(
C
f
0 0 1 0 3
)
(
D
f
75
.
1
av
f
0 1 0 1
0 0 1 0
0 1 0 0
0 0 1 1
1
)
(
X
f
4
)
(
Y
f
0 1 0 0 0 1 1 0 2
)
(
Z
f
10. Example (contd.)
After iteration 1:
After iteration 2:
0 1 0 1 2
)
(
A
f
0 1 1 0 2
)
(
B
f
0 0 1 0 3
)
(
C
f
0 0 1 1 4
)
(
D
f
75
.
2
av
f
0 1 0 1 2
)
(
A
f
0 0 0 1 3
)
(
B
f
0 0 1 0 3
)
(
C
f
0 0 1 1 4
)
(
D
f
3
av
f
0 1 0 1
0 0 1 0
0 1 1 0
0 0 0 1
2
)
(
X
f
3
)
(
Y
f
12. Schemas
Population
Strings over alphabet {0,1} of length L
E.g.
Schema
A schema is a subset of the space of all possible
individuals for which all the genes match the
template for schema H.
Strings over alphabet {0,1,*} of length L
E.g. }
11110
,
11010
,
10110
,
10010
{
]
10
*
*
1
[
H
10010
s
13. Hyper-plane model
Search space
A hyper-cube in L dimensional space
Individuals
Vertices of hyper-cube
Schemas
Hyper-planes formed by vertices
0**
14. Sampling Hyper-planes
Look for hyper-planes (schemas) with good
fitness value instead of vertices (individuals) to
reduce search space
Each vertex
Member of 3L hyper-planes
Samples hyper-planes
Average Fitness of a hyper-plane can be
estimated by sampling fitness of members in
population
Selection retains hyper-planes with good
estimated fitness values and discards others
15. Schema Theorem
Schema Order O(H)
Schema order, O(.) , is the number of non ‘*’ genes in
schema H.
E.g. O(1**1*) = 2
Schema Defining Length δ(H)
Schema Defining Length, δ(H), is the distance
between first and last non ‘*’ gene in schema H
E.g. δ(1**1*) = 4 – 1 = 3
Schemas with short defining length, low order with
fitness above average population are favored by
GAs
16. Formal Statement
Selection probability
Crossover probability
Mutation probability
Expected number of members of a schema
)
,
(
)
,
(
)
,
(
))
1
,
(
(
t
H
f
t
H
f
t
H
m
t
H
m
E
1
)
(
)
(
L
H
c
crossover p
h
P
m
mutation p
H
h
P )
(
)
(
))
(
1
)
(
1
(
)
,
(
)
,
(
)
,
(
)
1
,
(
( H
p
L
H
p
t
H
f
t
H
f
t
H
m
t
H
m
E m
c
17. Why crossover and mutation?
Crossover
Produces new solutions while ‘remembering’ the
characteristics of old solutions
Partially preserves distribution of strings across
schemas
Mutation
Randomly generates new solutions which cannot
be produced from existing population
Avoids local optimum
19. Area of application
GAs can be used when:
Non-analytical problems.
Non-linear models.
Uncertainty.
Large state spaces.
20. Non-analytical problems
Fitness functions may not be expressed
analytically always.
Domain specific knowledge may not be
computable from fitness function.
Scarce domain knowledge to guide the
search.
21. Non-linear models
Solutions depend on starting values.
Non – linear models may converge to local
optimum.
Impose conditions on fitness functions such as
convexity, etc.
May require the problem to be approximated to
fit the non-linear model.
22. Uncertainty
Noisy / approximated fitness functions.
Changing parameters.
Changing fitness functions.
Why do GAs work? Because uncertainty is
common in nature.
23. Large state spaces
Heuristics focus only on the immediate area of
initial solutions.
State-explosion problem: number of states
huge or even infinite! Too large to be handled.
State space may not be completely
understood.
24. Characteristics of GAs
Simple, Powerful, Adaptive, Parallel
Guarantee global optimum solutions.
Give solutions of un-approximated form of
problem.
Finer granularity of search spaces.
25. When not to use GA!
Constrained mathematical optimization
problems especially when there are few
solutions.
Constraints are difficult to incorporate into a
GA.
Guided domain search is possible and
efficient.
27. TSP Description
Problem Statement: Given a complete
weighted undirected graph, find the shortest
Hamiltonian cycle. (n nodes)
The size of the solution space in (n-1)!/2
Dynamic Programming gives us a solution in
time O(n22n)
TSP is NP Complete
28. TSP Encoding
Binary representation
Tour 1-3-2 is represented as ( 00 10 01 )
Path representation
Natural – ( 1 3 2 )
Adjacency representation
Tour 1-3-2 is represented as ( 3 1 2 )
Ordinal representation
A reference list is used. Let that be ( 1 2 3 ).
Tour 1-3-2 is represented as ( 1 2 1 )
29. TSP – Crossover operator
Order Based crossover (OX2)
Selects at random several positions in the parent
tour
Imposes the order of nodes in selected positions
of one parent on the other parent
Parents: (1 2 3 4 5 6 7 8) and (2 4 6 8 7 5 3 1)
Selected positions, 2nd , 3rd and 6th
Impose order on (2 4 6 8 7 5 3 1) &(1 2 3 4 5 6 7
8)
Children are (2 4 3 8 7 5 6 1) and (1 2 3 4 6 5 7 8)
30. TSP – Mutation Operators
Exchange Mutation Operator (EM)
Randomly select two nodes and interchange their
positions.
( 1 2 3 4 5 6 ) can become ( 1 2 6 4 5 3 )
Displacement Mutation Operator (DM)
Select a random sub-tour, remove and insert it in
a different location.
( 1 2 [3 4 5] 6 ) becomes ( 1 2 6 3 4 5 )
31. Conclusions
Plethora of applications
Molecular biology, scheduling, cryptography,
parameter optimization
General algorithmic model applicable to a
large variety of classes of problems
Another in the list of algorithms inspired by
biological processes – scope for more
parallels?
Philosophical Implication:
Are humans actually moving towards their global
optimum?
32. References
Adaptation in Natural and Artificial Systems,
John H. Holland, MIT Press, 1992.
Goldberg, D. E. 1989 Genetic Algorithms in
Search, Optimization and Machine Learning.
1st. Addison-Wesley Longman Publishing Co.,
Inc.
Genetic Algorithms for the Travelling
Salesman Problem: A Review of
Representations and Operators, P. Larranaga
et al., University of Basque, Spain. Artificial
Intelligence Review, Volume 13, Number 2 /
Editor's Notes
Most organisms evolve by means of two primary processes: natural selection and sexual reproduction. The first determines which members of population survive and reproduce, and the second ensures mixing and recombination among the genes of their offspring.
When sperm and ova fuse, matching chromosomes line up with one another and then cross-over partway along their length, thus swapping genetic material. This mixing allows creatures to evolve much more rapidly than they would if each offspring simply contained a copy of the genes of a single parent, modified occasionally by mutation.
High-quality strings mate; low-quality ones perish. As generations pass, strings associated with improved solutions will predominate. Furthermore, the mating process continually combines these strings in new ways, generating ever more sophisticated solutions.
Non-linear programming solvers generally use some form of gradient search technique to move along the steepest gradient until the highest point (maximisation) is reached. In the case of linear programming, a global optimum will always be attained (). However, non-linear programming models may be subject to problems of convergence to local optima, or in some cases, may be unable to find a feasible solution. This largely depends on the starting point of the solver.
1) Noisy fitness function. Noise in fitness evaluations may
come from many different sources such as sensory measurement
errors or randomized simulations.
2) Approximated fitness function. When the fitness function
is very expensive to evaluate, or an analytical fitness
function is not available, approximated fitness functions
are often used instead.
3) Robustness. Often, when a solution is implemented, the
design variables or the environmental parameters are subject
to perturbations or changes. Therefore, a common requirement
is that a solution should still work satisfyingly
either when the design variables change slightly, e.g., due
to manufacturing tolerances, or when the environmental
parameters vary slightly. This issue is generally known as
the search for robust solutions.
4) Dynamic fitness function. In a changing environment,
it should be possible to continuously track the moving
optimum rather than to repeatedly restart the optimization
process.
Iterative improvement techniques based
on module interchange are the most robust, simple and successful
heuristics in solving the partitioning and placement
problems. The main disadvantage of these heuristics is that
they mainly focus on the immediate area around the current
initial solution, thus no attempt is made to explore all regions
of the parameter space
The main practical limitation when model checking real systems is dealing
with the so-called state-explosion problem: the number of states contained in the
state space of large complex systems can be huge, even infinite, thereby making
exhaustive state-space exploration intractable.
The search space is large, complex or poorly understood. Domain knowledge is scarce or expert knowledge is difficult to encode to narrow the search space. No mathematical analysis is available.
Genetic Algorithms are adaptive to their environments.
timing improvement could be done by utilising the implicit parallelization of multiple independent generations evolving at the same time.
As in the example above, it would not be expected for a constrained mathematical programming problem to be solved faster by GA, which is a probabilistic search method, than by a traditional optimisation approach, which is a guided search method and has been developed and successfully applied to many models of this type over the years. Genetic algorithms should not be regarded as a replacement for other existing approaches, but as another optimisation approach which the modeller can use.
Unconstrained problems are particularly suitable for consideration as constraints require the management of possible infeasibility, which may slow down the optimisation process considerably. Generally, a standard genetic algorithm is taken for specific development of the problem under investigation where the modeller should take advantage of model structure for effective implementation.