The development and evaluation of an improved genetic algorithm based on migration and artificial selection

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Abstract
Much research has been done in developing improved genetic algorithms (GA's). Past research has focused on the improvement of operators and parameter settings and indicates that premature convergence is still the preeminent problem in GA's. This paper presents an improved genetic algorithm based on migration and artificial selection (GAMAS). GAMAS is an algorithm whose architecture is specifically designed to confront the causes of premature convergence. Though based on simple genetic algorithms, GAMAS is not concerned with the evolution of a single population, but instead is concerned with macroevolution, or the creation of multiple populations or species, and the derivation of solutions from the combined evolutionary effects of these species. New concepts that are emphasized in this architecture are artificial selection, migration, and recycling. Experimental results show that GAMAS consistently outperforms simple genetic algorithms and alleviates the problem of premature convergence.