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A Dispersion Operator for Geometric Semantic Genetic Programming

Published:20 July 2016Publication History

ABSTRACT

Recent advances in geometric semantic genetic programming (GSGP) have shown that the results obtained by these methods can outperform those obtained by classical genetic programming algorithms, in particular in the context of symbolic regression. However, there are still many open issues on how to improve their search mechanism. One of these issues is how to get around the fact that the GSGP crossover operator cannot generate solutions that are placed outside the convex hull formed by the individuals of the current population. Although the mutation operator alleviates this problem, we cannot guarantee it will find promising regions of the search space within feasible computational time. In this direction, this paper proposes a new geometric dispersion operator that uses multiplicative factors to move individuals to less dense areas of the search space around the target solution before applying semantic genetic operators. Experiments in sixteen datasets show that the results obtained by the proposed operator are statistically significantly better than those produced by GSGP and that the operator does indeed spread the solutions around the target solution.

References

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        cover image ACM Conferences
        GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016
        July 2016
        1196 pages
        ISBN:9781450342063
        DOI:10.1145/2908812

        Copyright © 2016 ACM

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        Publication History

        • Published: 20 July 2016

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        GECCO '16 Paper Acceptance Rate137of381submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

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