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C. FERREIRA In E. Lutton, J. A. Foster, J. Miller, C. Ryan, and A. G. B. Tettamanzi, eds., Proceedings of the 4th European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 2278, pages 51-60, Springer-Verlag, Berlin, Germany, 2002. Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming Conclusions   Gene expression programming is the most recent development on artificial evolutionary systems and one that brings about a considerable increase in performance due to the crossing of the phenotype threshold [14]. The crossing of the phenotype threshold allows the unconstrained exploration of the search space. Thus, in GEP, the implementation of high-performing search operators such as point mutation, transposition and recombination, is a child’s play as any modification made in the genome always results in valid phenotypes or programs. In this work, the recently invented algorithm was successfully applied to discover Boolean functions on seven-dimensional parameter spaces. The Boolean functions discovered by GEP consist of the solutions to the best known rules at coordinating the behavior of cellular automata in the density-classification task. The understanding of such complex behaviors is only possible if the programs behind the output bits of a CA rule are known. However, for the two rules analyzed here (Coevolution1 and Coevolution2), only the output bits were known. Therefore, the intelligible solutions to the most efficient CA rules at the density-classification task discovered in this work are most valuable for future research in complex emergent behavior. Home | Contents | Previous | Next