Study on Selection Methods of Parents and Crossover in Genetic Algorithm (original) (raw)

A Study of Genetic Algorithm and Crossover Techniques

IJCSMC, 2019

Genetic algorithms are inspired by Darwin's theory of natural evolution. In the natural world, organisms who are poorly suited for the environment die off, while those well suited, prosper. Genetic algorithms search the space of individuals for good candidates. The chance of particular individual being selected is proportional to the amount by which its fitness is greater or less than its competitors' fitness. Genetic algorithms are ways of solving problems by mimicking processes nature uses; i.e. Selection, Crossover, Mutation and accepting, to evolve a solution to a problem. Many crossover techniques exist for organism which uses different data structures to store themselves. Genetic algorithm which is one of the most well-known heuristic approaches, crossover components and crossover techniques, which are the most important property of the Genetic algorithms performance, has been discussed.

Study of Various Crossover Operators in Genetic Algorithms

2014

Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Different crossover and mutation operators exist to solve the problem that involves large population size. Example of such a problem is travelling sales man problem, which is having a large set of solution. In this paper we will discuss different crossover operators that help in solving the problem.

Evaluation of Crossover operators performance in Genetic Algorithms

— Genetic Algorithms are implemented in search and optimization techniques that were developed based on ideas and techniques from genetic and evolutionary theory. Beginning with a random population of chromosomes, a genetic algorithm chooses parents from which to generate offspring using operations like selection, crossover and mutation. Here, comparisons of 5 crossover operators that are used in genetic algorithms are performed. In performance of a genetic algorithm, crossover operator has an invaluable role. It is necessary to understand the role of the crossover operator. The purpose of this project is to compare larger set of crossover operators on the same test functions and evaluate their efficiency. It also includes evaluation of statistical tests in order to study the performance of the crossover operator.

Study on Genetic Algorithm and Implementation of Various Test Function using Crossover Operator

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2017

Nature has always been a great source of inspiration to all mankind. Genetic Algorithms(GAs) are search based algorithms based on the concepts of natural selection and genetics. GAs is a subset of a much larger branch of computation known as Evolutionary Computation. There are a number of operators available for each and every phase. Crossover is one such phase for which a variety of crossover operators exist. It becomes very confusing to select one crossover operator. Therefore, a comparative study of various crossover operators is provided to help researchers select any one of the available crossover operators. The study includes a deep study of various aspects of genetic algorithms and its phases. In this we can randomly create initial population and assign fitness value it depends on selection criteria. Then we create new offspring and calculate the domain value to find out the best operator

Improved Genetic Algorithm using New Crossover Operator

International Journal of Computing, Communication and Instrumentation Engineering, 2017

The complexity of existing crossover operators used in Genetic Algorithm is a critical factor that affects performance due to its negative impact on processing time. In this paper, a new crossover operator called Push and Pop Genes Exchange Operator (PPX) is introduced and its performance evaluated in terms of processing time. Results of comparative performance with six crossover operators show that PPX performed better in terms of processing time across various population size, with improvements ranging from 0.6% when compared to shuffle crossover at n=100 to 24.8% when compared to the half-uniform crossover operator at n=30. Results also show that PPX performed better with increase in population with a maximum of 13.1% when population was increased from 30 to 100. The results confirm that PPX improved the performance of Genetic Algorithm by reducing the complexity of crossover operation when compared to the existing operators.

Analysis of Selection Schemes for Solving an Optimization Problem in Genetic Algorithm

International Journal of Computer Applications, 2014

Genetic algorithm is a population based an adaptive search and optimizations techniques and genetic mimic the natural evolution process. The Genetic operators include selection, crossover and mutation. The aim to present this paper is it gives comparative selection strategies for solving an optimization problem in genetic algorithm and evaluates their performance.

CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW

The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of them. Crossover operators are mainly classified as application dependent crossover operators and application independent crossover operators. Effect of crossover operators in GA is application as well as encoding dependent. This paper will help researchers in selecting appropriate crossover operator for better results. The paper contains description about classical standard crossover operators, binary crossover operators, and application dependant crossover operators. Each crossover operator has its own advantages and disadvantages under various circumstances. This paper reviews the crossover operators proposed and experimented by various researchers.

Parent Selection Operators for Genetic Algorithms

2013

In this paper, an experimental study of six well known selection methods has conducted to a new technique of selection. Dynamic selection (DS), the proposed technique, exploits the advantages of each selection methods in terms of quality of solution and population diversity. Indeed, dynamic selection is based on two parameters that allow to decide the quality of candidate solutions and the genotypic diversity. The famous 0-1 Knapsack Problem is used to illustrate the efficiency of DS.

Correlation Study of Genetic Algorithm Operators: Crossover and Mutation Probabilities

Successful implementation of genetic algorithms depends on optimum values of several parameters but the most critical parameters are population size, crossover and mutation probability. Mechanism for determining the values of these critical parameters is not in existence. The best way to obtain the optimum values is to refer back to previous literature for guide. In this paper, we conduct a survey of optimum parameters values reported in literature. We further investigate the relationship between mutation and crossover probabilities. Several combinations of optimum parameter values are tabulated in this paper in order to serve as a guide for future researchers. It was revealed that crossover probability is positively associated with the use of mutation probability in the implementation of genetic algorithms but the correlation is not significant. 

A new crossover operator for genetic algorithms

Proceedings of IEEE International Conference on Evolutionary Computation, 1996

Starting from a mathematical reinterpretation of the classical crossover operator, a new type of crossover is introduced. The proposed new crossover operator gives better performances than the classical 1 point, 2 point or uniform crossover operators. In the paper a theorical investigation of the behaviour of the new crossover is presented. In comparison to the classical crossover operator, it allows a better exploration of the searching space and gives better findings. Some comparative results relative to the optimization of test functions taken from literature are given.