Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2012, Article ID 253714, 12 pages doi:10.1155/2012/253714 Research Article Neurogenetic Algorithm for Solving Combinatorial Engineering Problems M. Jalali Varnamkhasti 1 and Nasruddin Hassan 2 1 Department of Mathematics, Dolatabad Branch, Islamic Azad University, Isfahan 84318–11111, Iran 2 School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia Correspondence should be addressed to M. Jalali Varnamkhasti, jalali.m.v@gmail.com Received 15 April 2012; Revised 13 July 2012; Accepted 18 July 2012 Academic Editor: Hak-Keung Lam Copyright q 2012 M. Jalali Varnamkhasti and N. 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. 1. Introduction A large scale of design, control, scheduling, or other engineering problems results in solution of optimization problems. Genetic algorithms GAs were first considered by Holland 1. A genetic algorithm is a numerical optimization procedure that is based on evolutionary principles such as selection, recombination, and mutation. In many areas of chemistry, there are problems to which GAs can be used. For example, one of the principal subfield of analytical chemistry is the qualitative and quantitative identification of the main components of unknown mixtures by means of spectroscopic methods that investigate the molecules utilizing electromagnetic radiation. Genetic algorithms have been used here as they are effective at finding patterns in data even when the data contains a large amount of extraneous information. Genetic algorithms have also been used for the generation of regression curves,