Pareto Cooperative-Competitive Genetic Programming: A Classification Benchmarking Study
作者： Andrew R. McIntyre , Malcolm I. Heywood
DOI: 10.1007/978-0-387-87623-8_4
关键词:
摘要: A model for problem decomposition in Genetic Programming based classication is proposed consisting of four basic components: competitive coevolution, local Gaussian wrapper operators, evolutionary multiobjective (EMO) tness evaluation, and an explicitly cooperative objective. The framework specically emphasizes the relations between different components model. Thus, both operator objective work together to establish exemplar subsets against which performance evaluated domain achieved. Moreover, cost estimating over multiple objectives mitigated by ability associate specic exemplars with each classier.
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