Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression
dal Piccol Sotto, Leo Francoso [UNIFESP]
de Melo, Vinicius Veloso [UNIFESP]
Is part ofNeurocomputing
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Linear Genetic Programming (LGP) is an Evolutionary Computation algorithm, inspired in the Genetic Programming (GP) algorithm. Instead of using the standard tree representation of GP, LGP evolves a linear program, which causes a graph-based data flow with code reuse. LGP has been shown to outperform GP in several problems, including Symbolic Regression (SReg), and to produce simpler solutions. In this paper, we propose several LGP variants and compare them with a traditional LGP algorithm on a set of benchmark SReg functions from the literature. The main objectives of the variants were to both control bloat and privilege useful code in the population. Here we evaluate their effects during the evolution process and in the quality of the final solutions. Analysis of the results showed that bloat control and effective code maintenance worked, but they did not guarantee improvement in solution quality. (C) 2015 Elsevier B.V. All rights reserved.
CitationNeurocomputing. Amsterdam, v. 180, p. 79-93, 2016.
SponsorshipFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)
Comissao Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq Universal)
Coordenadoria de Apoio a Pesquisa e Ensino Superior (CAPES Science without Borders)
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