Too Busy to Learn
نویسندگان
چکیده
The goal of this research is to analyze how individual learning interacts with an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction two learning models, implemented as local search procedures, are proposed. Experimental results show that, in highly irregular and prone to premature convergence search spaces, local search methods are not an effective help to evolution. In addition, one interesting effect related to learning is reported. When the mutation rate is too high, learning acts as a repair, reintroducing some useful information that was lost.
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