The Influence of Learning in the Evolution of Busy Beavers



The goal of this research is to study how individual learning interacts with an evolutionary algorithm in its search for good candidates for the Busy Beaver problem. Two learning models, designed to act as local search procedures, are proposed. Experimental results show that local search methods that are able to perform several modifications in the structure of an individual in each learning step provide an important advantage. Some insight about the role that evolution and learning play during search is also presented.

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Year 2006 : 1 citations

 Paszkovicz, W.
Properties of a GA extended by a random self-learning operator and asymetric mutations: a convergence study for a task of powder-pattern indexing. Analytica Chimica Acta, 566 (1): 81-98 April 2006.