A Novel Collective Crossover Operator for Genetic Algorithms

Berna Kiraz; Azam Asilian Bidgoli; Hossein Ebrahimpour-Komleh; Shahryar Rahnamayan

doi:10.1109/SMC42975.2020.9282841

A Novel Collective Crossover Operator for Genetic Algorithms

Berna Kiraz
, Azam Asilian Bidgoli
, Hossein Ebrahimpour-Komleh
, Shahryar Rahnamayan

Fatih Sultan Mehmet Vakif Universitesi
University of Kashan
Ontario Tech University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among a variety of crossover operators, there has been a growing interest in multi-parent crossover operators in evolutionary computation. The main motivation of those schemes is establishing comprehensive collective collaboration of more than two chromosomes in the population to generate a new offspring. In this paper, a novel all-parent crossover operator called collective crossover for genetic algorithm is proposed. In this method, all individuals in the current population are involved in recombination part and one offspring is generated. The contribution of each individuals is defined based on its quality in terms of fitness value. The performance of the collective crossover operator is tested on CEC2017 benchmark functions. The results revealed that the proposed crossover operator performs better when compared to well-known two-parent crossover operators including one-point and two-point crossovers. In addition, the differences between collective crossover and the other crossover operators are statistically significant for the most cases.

Original language	English
Title of host publication	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4204-4209
Number of pages	6
ISBN (Electronic)	9781728185262
DOIs	https://doi.org/10.1109/SMC42975.2020.9282841
Publication status	Published - 11 Oct 2020
Externally published	Yes
Event	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada Duration: 11 Oct 2020 → 14 Oct 2020

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume	2020-October
ISSN (Print)	1062-922X

Conference

Conference	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/Territory	Canada
City	Toronto
Period	11/10/20 → 14/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

All-parent crossover
Crossover operator
Genetic algorithms
Multi-parent crossover
Optimization

Access to Document

10.1109/SMC42975.2020.9282841

Cite this

Kiraz, B., Bidgoli, A. A., Ebrahimpour-Komleh, H., & Rahnamayan, S. (2020). A Novel Collective Crossover Operator for Genetic Algorithms. In 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 (pp. 4204-4209). Article 9282841 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2020-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC42975.2020.9282841

Kiraz, Berna ; Bidgoli, Azam Asilian ; Ebrahimpour-Komleh, Hossein et al. / A Novel Collective Crossover Operator for Genetic Algorithms. 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 4204-4209 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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title = "A Novel Collective Crossover Operator for Genetic Algorithms",

abstract = "Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among a variety of crossover operators, there has been a growing interest in multi-parent crossover operators in evolutionary computation. The main motivation of those schemes is establishing comprehensive collective collaboration of more than two chromosomes in the population to generate a new offspring. In this paper, a novel all-parent crossover operator called collective crossover for genetic algorithm is proposed. In this method, all individuals in the current population are involved in recombination part and one offspring is generated. The contribution of each individuals is defined based on its quality in terms of fitness value. The performance of the collective crossover operator is tested on CEC2017 benchmark functions. The results revealed that the proposed crossover operator performs better when compared to well-known two-parent crossover operators including one-point and two-point crossovers. In addition, the differences between collective crossover and the other crossover operators are statistically significant for the most cases.",

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Kiraz, B, Bidgoli, AA, Ebrahimpour-Komleh, H & Rahnamayan, S 2020, A Novel Collective Crossover Operator for Genetic Algorithms. in 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020., 9282841, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 2020-October, Institute of Electrical and Electronics Engineers Inc., pp. 4204-4209, 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020, Toronto, Canada, 11/10/20. https://doi.org/10.1109/SMC42975.2020.9282841

A Novel Collective Crossover Operator for Genetic Algorithms. / Kiraz, Berna; Bidgoli, Azam Asilian; Ebrahimpour-Komleh, Hossein et al.
2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 4204-4209 9282841 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kiraz, Berna

AU - Bidgoli, Azam Asilian

AU - Ebrahimpour-Komleh, Hossein

AU - Rahnamayan, Shahryar

PY - 2020/10/11

Y1 - 2020/10/11

N2 - Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among a variety of crossover operators, there has been a growing interest in multi-parent crossover operators in evolutionary computation. The main motivation of those schemes is establishing comprehensive collective collaboration of more than two chromosomes in the population to generate a new offspring. In this paper, a novel all-parent crossover operator called collective crossover for genetic algorithm is proposed. In this method, all individuals in the current population are involved in recombination part and one offspring is generated. The contribution of each individuals is defined based on its quality in terms of fitness value. The performance of the collective crossover operator is tested on CEC2017 benchmark functions. The results revealed that the proposed crossover operator performs better when compared to well-known two-parent crossover operators including one-point and two-point crossovers. In addition, the differences between collective crossover and the other crossover operators are statistically significant for the most cases.

AB - Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among a variety of crossover operators, there has been a growing interest in multi-parent crossover operators in evolutionary computation. The main motivation of those schemes is establishing comprehensive collective collaboration of more than two chromosomes in the population to generate a new offspring. In this paper, a novel all-parent crossover operator called collective crossover for genetic algorithm is proposed. In this method, all individuals in the current population are involved in recombination part and one offspring is generated. The contribution of each individuals is defined based on its quality in terms of fitness value. The performance of the collective crossover operator is tested on CEC2017 benchmark functions. The results revealed that the proposed crossover operator performs better when compared to well-known two-parent crossover operators including one-point and two-point crossovers. In addition, the differences between collective crossover and the other crossover operators are statistically significant for the most cases.

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Kiraz B, Bidgoli AA, Ebrahimpour-Komleh H, Rahnamayan S. A Novel Collective Crossover Operator for Genetic Algorithms. In 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 4204-4209. 9282841. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC42975.2020.9282841

A Novel Collective Crossover Operator for Genetic Algorithms

Abstract

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Bibliographical note

Keywords

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