Self-adaption is one of the most promising areas of research in evolutionary computation as it adapts the algorithm to the problem while solving the problem. In this paper we extend self-adaption to operate on more than one aspect of evolutionary computation and at more than one level of adaption. We developed a genetic algorithm which self-adapts both mutation strength and population size; the results indicate that the approach works quite well.
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