Schema Co-Evolutionary Algorithm (SCEA)(Algorithms)

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概要

• 論文の詳細を見る

• Simple genetic algorithm (SGA) is apopulation-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.

• 社団法人電子情報通信学会の論文
• 2004-02-01

著者

• Sim Kwee-bo

  School Of Electrical And Electronic Engineering Chung-ang University

• Lee Dong-wook

  School Of Electrical And Electronic Engineering Chung-ang University

関連論文

• Game Theory Based Co-evolutionary Algorithm (GCEA) for Solving Multiobjective Optimization Problems(Artificial Intelligence and Cognitive Science)
• Self-Nonself Recognition Algorithm Based on Positive and Negative Selection(Applications of Information Security Techniques)
• Schema Co-Evolutionary Algorithm (SCEA)(Algorithms)

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