Journal of Applied Mathematics
Volume 2012 (2012), Article ID 253714, 12 pages
Research Article

Neurogenetic Algorithm for Solving Combinatorial Engineering Problems

1Department of Mathematics, Dolatabad Branch, Islamic Azad University, Isfahan 84318–11111, Iran
2School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia

Received 15 April 2012; Revised 13 July 2012; Accepted 18 July 2012

Academic Editor: Hak-Keung Lam

Copyright © 2012 M. Jalali Varnamkhasti and Nasruddin Hassan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.