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21 March 2003 Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments
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Abstract
An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.
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James F. Smith III and Joseph A. Blank "Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments", Proc. SPIE 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V, (21 March 2003); https://doi.org/10.1117/12.486034
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