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A memetic evolutionary search algorithm with variable length chromosome for rule extraction

This paper proposes a new memetic evolutionary approach for rule extraction from datasets. The evolutionary algorithm integrated an adaptive micro-search intensity scheme inspired by artificial immune system (AIS) for local fine-tuning of the rules. In addition, the rules are encoded using variable length representation allowing easy adaptation. Through the structural mutation and crossover operators, the appropriate number of rules is optimized. Simulation results of the proposed method on real world benchmarking datasets demonstrated the effectiveness of the algorithm.