Decreasing the Number of Evaluations in Evolutionary Algorithms by Using a Meta-model of the Fitness Function
نویسندگان
چکیده
In this paper a method is presented that decreases the necessary number of evaluations in Evolutionary Algorithms. A classifier with confidence information is evolved to replace time consuming evaluations during tournament selection. Experimental analysis of a mathematical example and the application of the method to the problem of evolving walking patterns for quadruped robots show the potential of the presented approach.
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