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A novel and effictive GA algorithm to solve optimization problem
Classical geneticalgorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient geneticalgorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and.
Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (geneticalgorithm) wrappers for feature selection. WEKA 3 interfaces are in development.