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Schema Co-Evolutionary Algorithm (SCEA)
Kwee-Bo SIM Dong-Wook LEE
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2004/02/01
Print ISSN: 0916-8532
Type of Manuscript: PAPER
simple genetic algorithm (SGA), schema co-evolutionary algorithm (SCEA), schema theorem, building block hypothesis, Walsh-schema transform,
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Simple genetic algorithm (SGA) is a population-based optimization method based on the Darwinian natural selection. The theoretical foundations of SGA are the Schema Theorem and the Building Block Hypothesis. Although SGA does well in many applications as an optimization method, it still does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. As an alternative schema, therefore, there is a growing interest in a co-evolutionary system where two populations constantly interact and cooperate each other. In this paper we propose a schema co-evolutionary algorithm (SCEA) and show why the SCEA works better than SGA in terms of an extended schema theorem. The experimental analyses using the Walsh-Schema Transform show that the SCEA works well in GA-hard problems including deceptive problems.