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Neurogenetic Algorithm for Solving Combinatorial Engineering Problems

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  • M. Jalali Varnamkhasti
  • Nasruddin Hassan

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.

Suggested Citation

  • M. Jalali Varnamkhasti & Nasruddin Hassan, 2012. "Neurogenetic Algorithm for Solving Combinatorial Engineering Problems," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:253714
    DOI: 10.1155/2012/253714
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    References listed on IDEAS

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    1. M. Jalali Varnamkhasti & L. S. Lee, 2012. "A Fuzzy Genetic Algorithm Based on Binary Encoding for Solving Multidimensional Knapsack Problems," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-23, April.
    2. M. Jalali Varnamkhasti & L. S. Lee, 2012. "A Fuzzy Genetic Algorithm Based on Binary Encoding for Solving Multidimensional Knapsack Problems," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
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