Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression

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@Article{Sotto:2016:Neurocomputing,

• author = "Leo Francoso dal Piccol Sotto and Vinicius Veloso de Melo",
• title = "Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression",
• journal = "Neurocomputing",
• volume = "180",
• pages = "79--93",
• year = "2016",
• note = "Progress in Intelligent Systems Design Selected papers from the 4th Brazilian Conference on Intelligent Systems (BRACIS 2014)",
• ISSN = "0925-2312",
• DOI = "doi:10.1016/j.neucom.2015.10.109",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925231215015866",
• abstract = "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.",
• keywords = "genetic algorithms, genetic programming, Bloat control, Effective code, Symbolic regression",

}

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Vinicius Veloso de Melo

Citations