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A Dispersion Operator for Geometric Semantic Genetic Programming

Oliveira, Luiz O.V.B., Otero, Fernando E.B., Pappa, Gisele L. (2016) A Dispersion Operator for Geometric Semantic Genetic Programming. In: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation. . pp. 773-780. ACM Press (doi:10.1145/2908812.2908923) (KAR id:55156)

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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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/2908812.2908923
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 26 Apr 2016 15:23 UTC
Last Modified: 16 Feb 2021 13:34 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
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