Genetic algorithms are widely used as optimization and adaptation tools, and they became important in artificial intelligence. Even though several successful applications have been reported, recent research has identified some inefficiencies in genetic algorithm performance. This paper argues that the degradation of genetic algorithm performance originates from the random application of the variation operators, since resampling of already visited points is not avoided. Consequently, this paper proposes an algorithmic framework, the "deterministic" genetic algorithm, that yields significantly faster convergence.
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