A linear estimation-of-distribution GP system

Authors:
Riccardo Poli;Nicholas F. McPhee
Affiliations:
Department of Computing and Electronic Systems, University of Essex, UK;Division of Science and Mathematics, University of Minnesota, Morris
Venue:
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Year:
2008

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Abstract

We present N-gram GP, an estimation of distribution algorithm for the evolution of linear computer programs. The algorithm learns and samples a joint probability distribution of triplets of instructions (or 3-grams) at the same time as it is learning and sampling a program length distribution. We have tested N-gram GP on symbolic regressions problems where the target function is a polynomial of up to degree 12 and lawn-mower problems with lawn sizes of up to 12×12. Results show that the algorithm is effective and scales better on these problems than either linear GP or simple stochastic hill-climbing.