Journal of Applied Mathematics

Neurogenetic Algorithm for Solving Combinatorial Engineering Problems

M. Jalali Varnamkhasti and Nasruddin Hassan

Full-text: Open access

Abstract

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.

Article information

Source
J. Appl. Math., Volume 2012 (2012), Article ID 253714, 12 pages.

Dates
First available in Project Euclid: 2 January 2013

Permanent link to this document
https://projecteuclid.org/euclid.jam/1357153501

Digital Object Identifier
doi:10.1155/2012/253714

Zentralblatt MATH identifier
1251.90406

Citation

Jalali Varnamkhasti, M.; Hassan, Nasruddin. Neurogenetic Algorithm for Solving Combinatorial Engineering Problems. J. Appl. Math. 2012 (2012), Article ID 253714, 12 pages. doi:10.1155/2012/253714. https://projecteuclid.org/euclid.jam/1357153501


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