Detecting Convergence in Genetic Algorithms with Decreasing Mutation Policies

S.E. García (Ecuador), M. Saad, and O. Akhrif (Canada)


Genetic Algorithms, Diversity Index, Convergence, Global Optimization, Optimization.


The efficiency and effectiveness of Real-Coded Genetic Al gorithms (RCGAs) are highly determined by the degree of exploitation and exploration kept throughout the run. In the cases that RCGAs use mutation operators whose rates decrease with the number of generations, having a metric of dissimilarity is very important. This metric may allow to evaluate the effectiveness of the crossover operator as the number of mutations at each iteration decreases, and it may provide a way to detect the convergence of the al gorithm. The present work introduces a new method of detecting convergence of RCGAs that use decreasing mu tation policies. This new procedure consists in the measur ing of the diversity of the individuals of each generation by applying the modified Simpson’s Diversity index, which is often used to quantify the biodiversity of a habitat in ecol ogy.

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