Neurogenetic Algorithm for Solving Combinatorial Engineering Problems
Diversity of the population in a genetic algorithm plays an important role in impeding premature convergence. This paper proposes an adaptive neurofuzzy inference system genetic algorithm based on sexual selection. In this technique, for choosing the female chromosome during sexual selection, a bili...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2012/253714 |
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| _version_ | 1832564849206362112 |
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| author | M. Jalali Varnamkhasti Nasruddin Hassan |
| author_facet | M. Jalali Varnamkhasti Nasruddin Hassan |
| author_sort | M. Jalali Varnamkhasti |
| collection | DOAJ |
| description | Diversity of the population in a genetic algorithm plays an important role in impeding premature convergence. This paper proposes an adaptive neurofuzzy inference system genetic algorithm based on sexual selection. In this technique, for choosing the female chromosome during sexual selection, a bilinear allocation lifetime approach is used to label the chromosomes based on their fitness value which will then be used to characterize the diversity of the population. The motivation of this algorithm is to maintain the population diversity throughout the search procedure. To promote diversity, the proposed algorithm combines the concept of gender and age of individuals and the fuzzy logic during the selection of parents. In order to appraise the performance of the techniques used in this study, one of the chemistry problems and some nonlinear functions available in literature is used. |
| format | Article |
| id | doaj-art-b755dfcc1c874c78a83dbb48b46895ca |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-b755dfcc1c874c78a83dbb48b46895ca2025-02-03T01:10:09ZengWileyJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/253714253714Neurogenetic Algorithm for Solving Combinatorial Engineering ProblemsM. Jalali Varnamkhasti0Nasruddin Hassan1Department of Mathematics, Dolatabad Branch, Islamic Azad University, Isfahan 84318–11111, IranSchool of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, MalaysiaDiversity of the population in a genetic algorithm plays an important role in impeding premature convergence. This paper proposes an adaptive neurofuzzy inference system genetic algorithm based on sexual selection. In this technique, for choosing the female chromosome during sexual selection, a bilinear allocation lifetime approach is used to label the chromosomes based on their fitness value which will then be used to characterize the diversity of the population. The motivation of this algorithm is to maintain the population diversity throughout the search procedure. To promote diversity, the proposed algorithm combines the concept of gender and age of individuals and the fuzzy logic during the selection of parents. In order to appraise the performance of the techniques used in this study, one of the chemistry problems and some nonlinear functions available in literature is used.http://dx.doi.org/10.1155/2012/253714 |
| spellingShingle | M. Jalali Varnamkhasti Nasruddin Hassan Neurogenetic Algorithm for Solving Combinatorial Engineering Problems Journal of Applied Mathematics |
| title | Neurogenetic Algorithm for Solving Combinatorial Engineering Problems |
| title_full | Neurogenetic Algorithm for Solving Combinatorial Engineering Problems |
| title_fullStr | Neurogenetic Algorithm for Solving Combinatorial Engineering Problems |
| title_full_unstemmed | Neurogenetic Algorithm for Solving Combinatorial Engineering Problems |
| title_short | Neurogenetic Algorithm for Solving Combinatorial Engineering Problems |
| title_sort | neurogenetic algorithm for solving combinatorial engineering problems |
| url | http://dx.doi.org/10.1155/2012/253714 |
| work_keys_str_mv | AT mjalalivarnamkhasti neurogeneticalgorithmforsolvingcombinatorialengineeringproblems AT nasruddinhassan neurogeneticalgorithmforsolvingcombinatorialengineeringproblems |