Understanding the Role of Learning in the Evolution of Busy Beavers: a Comparison Between the Baldwin Effect and a Lamarckian Strategy
نویسندگان
چکیده
In this paper we study how individual learning interacts with an evolutionary algorithm in its search for good solutions to the Busy Beaver problem. Two learning strategies, the Baldwin Effect and Lamarckian learning, are compared with an extensive set of experiments. Results show that the Baldwin Effect is less sensitive to specific issues concerning the definition of the learning model and it is more effective in adjusting its learning power to maximise the search performance of the evolutionary algorithm. Some insight about the specific role that evolution and learning play during search is also presented.
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