A Generic Framework for Building Dispersion Operators in the Semantic Space

Oliveira, Luiz O.V.B. and Otero, Fernando E.B. and Pappa, Gisele L. (2016) A Generic Framework for Building Dispersion Operators in the Semantic Space. In: Genetic Programming Theory and Practice XIV. Springer. (In press) (Full text available)

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Abstract

This chapter proposes a generic framework to build geometric dispersion (GD) operators for Geometric Semantic Genetic Programming in the context of symbolic regression, followed by two concrete instantiations of the framework: a multiplicative geometric dispersion operator and an additive geometric dispersion operator. These operators move individuals in the semantic space in order to balance the population around the target output in each dimension, with the objective of expanding the convex hull defined by the population to include the desired output vector. An experimental analysis was conducted in a testbed composed of sixteen datasets showing that dispersion operators can improve GSGP search and that the multiplicative version of the operator is overall better than the additive version.

Item Type: Book section
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 30 Nov 2016 13:49 UTC
Last Modified: 08 May 2018 09:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/59297 (The current URI for this page, for reference purposes)
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