Neurogenetic algorithm for solving combinatorial engineering problems

M. Jalali Varnamkhasti, Nasruddin Hassan

Research output: Contribution to journalArticle

31 Citations (Scopus)

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.

Original languageEnglish
Article number253714
JournalJournal of Applied Mathematics
Volume2012
DOIs
Publication statusPublished - 2012

Fingerprint

Chromosomes
Genetic algorithms
Engineering
Chromosome
Genetic Algorithm
Fuzzy logic
Labels
Population Diversity
Adaptive Neuro-fuzzy Inference System
Premature Convergence
Nonlinear Function
Chemistry
Fitness
Fuzzy Logic
Lifetime

ASJC Scopus subject areas

  • Applied Mathematics

Cite this

Neurogenetic algorithm for solving combinatorial engineering problems. / Jalali Varnamkhasti, M.; Hassan, Nasruddin.

In: Journal of Applied Mathematics, Vol. 2012, 253714, 2012.

Research output: Contribution to journalArticle

Jalali Varnamkhasti, M. ; Hassan, Nasruddin. / Neurogenetic algorithm for solving combinatorial engineering problems. In: Journal of Applied Mathematics. 2012 ; Vol. 2012.
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