Parallel hybrid evolutionary computation: Automatic tuning of parameters for parallel gene expression programming

초록

A parallel hybrid framework that combines gene expression programming (GEP) as the evolutionary problem-solving methodology and alternative meta-heuristics for tuning parameter values of the parallel GEP runs is presented. The implementation of this framework is based on a client-server architecture which includes clients that use GEP to evolve candidate solutions for the problem in question, and clients that use (possibly) other meta-heuristics to tune GEP input parameters. In the implementation of this framework, a genetic algorithms methodology is used for parameter tuning. For testing the framework and its implementation, a suite of symbolic regression problems of different complexities is used. Our experimental results show that our approach provides a solution for the problem of automatically tuning two GEP input parameters, viz., the number of genes and the length of each gene. (C) 2007 Elsevier Inc. All rights reserved.

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