Evaluation of Crossover operators performance in Genetic Algorithms (original) (raw)

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.

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.

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.

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

Comparative Performances of Crossover Functions in Genetic Algorithms

Genetic Algorithms have been widely applied to various kinds of optimisation problems. In this work, a Genetic Algorithm is designed to solve the three classic numerical optimisation problems – Rastrigin, Schwefel and Griewank. An experiment to observe the comparative performances of five different crossover functions was conducted. Also, the possible effect of aging out some of the old individuals from the population was hinted at. A parameter set expected to give the optimal performance and a discussion on the design considerations are presented below

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. 

Some Further Experiments with Crossover Operators for Genetic Algorithms

Informatica, 2018

Crossover operators play a very important role by creation of genetic algorithms (GAs) which are applied in various areas of computer science, including combinatorial optimization. In this paper, fifteen genetic crossover procedures are designed and implemented using a modern C# programming language. The computational experiments have been conducted with these operators by solving the famous combinatorial optimization problem-the quadratic assignment problem (QAP). The results of the conducted experiments on the characteristic benchmark instances from the QAP instances library QAPLIB illustrate the relative performance of the examined crossover operations. All crossover procedures are publicly available with the intention that the GA researchers will choose a procedure which suits the individual demand at the highest degree.

Genetic Algorithm and Efficiency of different crossovers

Brock University, 2022

This experiment examined the efficiency of Uniform Ordered Crossover and One-Point Crossover in Genetic Algorithms. By performing various tests on documents Shredded-1, Shredded-2 and Shredded-3, I will compare the average and best fitness of various chromosomes using the Uniform Ordered Crossover and One-Point Crossover. Moreover, scores of each generation are calculated to ensure the algorithm satisfies Charles Darwin theory of evolution. The various tests in the Genetic Algorithms are conducted over various population sizes. Crossover and Mutations are performed on these randomized populations and offspring are generated.

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.

Study on Selection Methods of Parents and Crossover in Genetic Algorithm

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Genetic Algorithms are the population based search and optimization technique that mimic the process of Genetic and Natural Evolution. Genetic algorithms are very effective way of finding an Optimized solution to a complex problem. Performance of genetic algorithms mainly depends on various factors such as selection of efficient parents and type of genetic operators which involve crossover and mutation operators etc. This paper will help the people to acquire the knowledge about various strategies of selecting parents and description about standard crossover operators.