Operator Adaptation in Structure Optimization of Neural Networks
نویسندگان
چکیده
The ability of an evolutionary algorithm (EA) to adapt its search strategy during the optimization process is a central concept in evolutionary computation, because (i) the best setting of an EA is not known a priori for a given task, (ii) the optimal search strategy is normally not constant during the evolutionary process. Thinking in terms of search (or generating) distributions, population and search strategy as well as selection and strategy adaptation are of equal importance. We propose a derandomized algorithm that adapts operator probabilities on population level. A detailed description, theoretical and more empirical results, and in particular references to related work can be found in [1].
منابع مشابه
Operator adaptation in evolutionary computation and its application to structure optimization of neural networks
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