Crossover and mutation operators of genetic algorithms
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in...
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| Format: | Article |
| Language: | English |
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IACSIT Press
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/53868/ http://psasir.upm.edu.my/id/eprint/53868/1/Crossover%20and%20mutation%20operators%20of%20genetic%20algorithms.pdf |
| _version_ | 1848852396737822720 |
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| author | Lim, Siew Mooi Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, Kuan Yew |
| author_facet | Lim, Siew Mooi Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, Kuan Yew |
| author_sort | Lim, Siew Mooi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole. |
| first_indexed | 2025-11-15T10:37:25Z |
| format | Article |
| id | upm-53868 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:37:25Z |
| publishDate | 2017 |
| publisher | IACSIT Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-538682019-05-10T04:28:16Z http://psasir.upm.edu.my/id/eprint/53868/ Crossover and mutation operators of genetic algorithms Lim, Siew Mooi Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, Kuan Yew Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole. IACSIT Press 2017-02 Article NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/53868/1/Crossover%20and%20mutation%20operators%20of%20genetic%20algorithms.pdf Lim, Siew Mooi and Md. Sultan, Abu Bakar and Sulaiman, Md. Nasir and Mustapha, Aida and Leong, Kuan Yew (2017) Crossover and mutation operators of genetic algorithms. International Journal of Machine Learning and Computing, 7 (1). pp. 9-12. ISSN 2010-3700 http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=69&id=704 10.18178/ijmlc.2017.7.1.611 |
| spellingShingle | Lim, Siew Mooi Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, Kuan Yew Crossover and mutation operators of genetic algorithms |
| title | Crossover and mutation operators of genetic algorithms |
| title_full | Crossover and mutation operators of genetic algorithms |
| title_fullStr | Crossover and mutation operators of genetic algorithms |
| title_full_unstemmed | Crossover and mutation operators of genetic algorithms |
| title_short | Crossover and mutation operators of genetic algorithms |
| title_sort | crossover and mutation operators of genetic algorithms |
| url | http://psasir.upm.edu.my/id/eprint/53868/ http://psasir.upm.edu.my/id/eprint/53868/ http://psasir.upm.edu.my/id/eprint/53868/ http://psasir.upm.edu.my/id/eprint/53868/1/Crossover%20and%20mutation%20operators%20of%20genetic%20algorithms.pdf |