survey

A Survey on Crossover Operators

Authors:

T. V. GeethaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 49, Issue 4

Article No.: 72, Pages 1 - 43

https://doi.org/10.1145/3009966

Published: 20 December 2016 Publication History

Abstract

Crossover is an important operation in the Genetic Algorithms (GA). Crossover operation is responsible for producing offspring for the next generation so as to explore a much wider area of the solution space. There are many crossover operators designed to cater to different needs of different optimization problems. Despite the many analyses, it is still difficult to decide which crossover to use when. In this article, we have considered the various existing crossover operators based on the application for which they were designed for and the purpose that they were designed for. We have classified the existing crossover operators into two broad categories, namely (1) Crossover operators for representation of applications -- where the crossover operators designed to suit the representation aspect of applications are discussed along with how the crossover operators work and (2) Crossover operators for improving GA performance of applications -- where crossover operators designed to influence the quality of the solution and speed of GA are discussed. We have also come up with some interesting future directions in the area of designing new crossover operators as a result of our survey.

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Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to ...
On the efficiency of crossover operators in genetic algorithms with binary representation
NN'10/EC'10/FS'10: Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems

Genetic Algorithm (GA) represents robust, adaptive method successfully applied to various optimization problems. To evaluate the performance of the genetic algorithm, it is common to use some kind of test functions. However, the "no free lunch" theorem ...

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 49, Issue 4

December 2017

666 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3022634

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 December 2016

Accepted: 01 October 2016

Revised: 01 July 2016

Received: 01 August 2015

Published in CSUR Volume 49, Issue 4

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