Abstract
Our purpose is to enhance performance of Genetic Programming (GP) search. For this, we have been develop a homogeneous system allowing to construct simultaneously a solution and sub-parts of it within a GP framework. This problem is a crucial point in GP research lately since this is intimately linked with building blocks existence problem. Thus, in this paper, we present an “on-going” work concerning DL GP — Dynamic Lattice Genetic Programming— a new GP system to evolve shared specific modules using a hierarchical cooperative coevolution paradigm. This scheme attempts to improve efficiency of GP by taking one’s inspiration of organization of natural entities, especially the emergence of complexity. In particular, DL GP does not require heuristic knowledge. Different credit assignment strategies are presented to compute modules fitness.
DLGP approach attempts to reduce the global depth of a tree-solution and avoids multiple searches of the same sub-components. Moreover modules induction improves “readability” of GP outputs. In particular, local evolutionary process is applied on the different set of subroutines in order to do converged each population toward a specific ability which remains at disposal of higher level subroutines. Problem decomposition and sub-tasks distribution is emergent through the lattice.
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Racine, A., Schoenauer, M., Dague, P. (1998). A dynamic lattice to envolve hierarchically shared subroutines. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds) Genetic Programming. EuroGP 1998. Lecture Notes in Computer Science, vol 1391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0055941
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DOI: https://doi.org/10.1007/BFb0055941
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