Condition Matrix Based Genetic Programming for Rule Learning

Most genetic programming paradigms are population-based and require huge amount of memory. In this paper, we review the instruction matrix based genetic programming which maintains all program components in a instruction matrix (IM) instead of manipulating a population of programs. A genetic program is extracted from the matrix just before it is being evaluated. After each evaluation, the fitness of the genetic program is propagated to its corresponding cells in the matrix. Then, we extend the instruction matrix to the condition matrix (CM) for generating rule base from datasets. CM keeps some of characteristics of IM and incorporates the information about rule learning. In the evolving process, we adopt an elitist idea to keep the better rules alive to the end. We consider that genetic selection maybe lead to the huge size of rule set, so the reduct theory borrowed from rough sets is used to cut the volume of rules and keep the same fitness as the original rule set. In experiments, we compare the performance of condition matrix for rule learning (CMRL) with other traditional algorithms. Results are presented in detail and the competitive advantage and drawbacks of CMRL are discussed

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