ANFISGA -Adaptive Neuro-Fuzzy Inference System Genetic Algorithm

s In optimization, when the genetic algorithm fails to find the global optimum, the problem is often credited to premature convergence. Premature convergence is influenced by different parameters. One of the important parameters is diversity population. In this study, we use a novel method to keep diversity in population. A new technique for choosing the female chromosome during sexual selection in a genetic algorithm is proposed. A bi-linear allocation lifetime approach is used to label the chromosomes based on their fitness value. The label will then be used to characterize the diversity of the population. During the sexual selection, the male chromosome is selected randomly. The label of the selected male chromosome and the population diversity of the previous generation are then applied within a set of fuzzy rules and Adaptive Neuro-Fuzzy Inference System Genetic Algorithm to select a suitable female chromosome for recombination. Extensive computational experiments are conducted to assess the performance of the proposed technique with some commonly used sexual selection mechanisms found in a standard GA for solving some numerical functions from the literature. The computational results show that the proposed technique produces higher solutions quality compared to others.