30 March 2000 Case studies in applying fitness distributions in evolutionary algorithms: I. Simple neural networks and Gaussion mutation
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Abstract
Evolutionary algorithms are often applied to tasks where the challenges is to find a superior solution. The engineering challenge concerns how to best design such algorithms in terms of their representation, variation operators, and selection. The distribution of fitness scores that is obtained when applying variation operators to parents can provide useful information for setting the parameters that are associated with those operators. Experiments presented here indicate that fitness distributions can also reveal information about the landscapes that surrounds particular parents and suggest that typical methods of self-adaption may not be very well suited for exploring the state space of possible solutions in the presence of multiple minima.
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Ankit Jain, Ankit Jain, David B. Fogel, David B. Fogel, } "Case studies in applying fitness distributions in evolutionary algorithms: I. Simple neural networks and Gaussion mutation", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380569; https://doi.org/10.1117/12.380569
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