A genetic algorithm with fuzzy crossover operator and probability
The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for...
| Huvudupphovsmän: | , , , |
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| Materialtyp: | Artikel |
| Språk: | English |
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Hindawi Publishing Corporation
2012
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| Länkar: | http://psasir.upm.edu.my/id/eprint/25243/1/25243.pdf |
| _version_ | 1825925738558128128 |
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| author | Varnamkhasti, Mohammad Jalali Lee, Lai Soon Abu Bakar, Mohd Rizam Leong, Wah June |
| author_facet | Varnamkhasti, Mohammad Jalali Lee, Lai Soon Abu Bakar, Mohd Rizam Leong, Wah June |
| author_sort | Varnamkhasti, Mohammad Jalali |
| collection | UPM |
| description | The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probability selection technique is proposed based on the population diversity using a fuzzy logic controller. The measurement of the population diversity is based on the genotype and phenotype properties. In this fuzzy inference system, the selection of the crossover operator and its probability are controlled by a set of fuzzy rules derived from the fuzzy logic controller. Extensive computational experiments are conducted on the proposed algorithm, and the results are compared with some crossover operators commonly used for solving multidimensional 0/1 knapsack problems published in the literature. The results indicate that the proposed algorithm is effective in finding better quality solutions. |
| first_indexed | 2024-03-06T08:02:15Z |
| format | Article |
| id | upm.eprints-25243 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:02:15Z |
| publishDate | 2012 |
| publisher | Hindawi Publishing Corporation |
| record_format | dspace |
| spelling | upm.eprints-252432019-10-11T06:42:03Z http://psasir.upm.edu.my/id/eprint/25243/ A genetic algorithm with fuzzy crossover operator and probability Varnamkhasti, Mohammad Jalali Lee, Lai Soon Abu Bakar, Mohd Rizam Leong, Wah June The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probability selection technique is proposed based on the population diversity using a fuzzy logic controller. The measurement of the population diversity is based on the genotype and phenotype properties. In this fuzzy inference system, the selection of the crossover operator and its probability are controlled by a set of fuzzy rules derived from the fuzzy logic controller. Extensive computational experiments are conducted on the proposed algorithm, and the results are compared with some crossover operators commonly used for solving multidimensional 0/1 knapsack problems published in the literature. The results indicate that the proposed algorithm is effective in finding better quality solutions. Hindawi Publishing Corporation 2012 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/25243/1/25243.pdf Varnamkhasti, Mohammad Jalali and Lee, Lai Soon and Abu Bakar, Mohd Rizam and Leong, Wah June (2012) A genetic algorithm with fuzzy crossover operator and probability. Advances in Operations Research, 2012 (956498). pp. 1-16. ISSN 1687-9147; ESSN: 1687-9155 https://www.hindawi.com/journals/aor/2012/956498/ 10.1155/2012/956498 |
| spellingShingle | Varnamkhasti, Mohammad Jalali Lee, Lai Soon Abu Bakar, Mohd Rizam Leong, Wah June A genetic algorithm with fuzzy crossover operator and probability |
| title | A genetic algorithm with fuzzy crossover operator and probability |
| title_full | A genetic algorithm with fuzzy crossover operator and probability |
| title_fullStr | A genetic algorithm with fuzzy crossover operator and probability |
| title_full_unstemmed | A genetic algorithm with fuzzy crossover operator and probability |
| title_short | A genetic algorithm with fuzzy crossover operator and probability |
| title_sort | genetic algorithm with fuzzy crossover operator and probability |
| url | http://psasir.upm.edu.my/id/eprint/25243/1/25243.pdf |
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