Lamarckian Learning in Multi-Agent Environments
نویسنده
چکیده
Genetic algorithms gain much of their power from mechanisms derived from the field of population genetics. However, it is possible, and in some cases desirable, to augment the standard mechanisms with additional features not available in biological systems. In this paper, we examine the use of Lamarckian learning operators in the SAMUEL architecture. The use of the operators is illustrated on three tasks in multi-agent environments.
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تاریخ انتشار 1991