Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Pawlak:cgsgp:EC,

• author = "Tomasz P. Pawlak and Krzysztof Krawiec",
• title = "Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis",
• journal = "Evolutionary Computation",
• year = "2018",
• volume = "26",
• number = "2",
• pages = "177--212",
• month = "Summer",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/EVCO_a_00205",
• size = "37 pages",
• abstract = "Program semantics is a promising recent research thread in Genetic Programming (GP). Over a dozen of semantic-aware search, selection, and initialization operators for GP have been proposed to date. Some of those operators are designed to exploit the geometric properties of semantic space, while some others focus on making offspring effective, i.e., semantically different from their parents. Only a small fraction of previous works aimed at addressing both these features simultaneously. In this paper, we propose a suite of competent operators that combine effectiveness with geometry for population initialization, mate selection, mutation and crossover. We present a theoretical rationale behind these operators and compare them experimentally to operators known from literature on symbolic regression and Boolean function synthesis benchmarks. We analyse each operator in isolation as well as verify how they fare together in an evolutionary run, concluding that the competent operators are superior on a wide range of performance indicators, including best-of-run fitness, test-set fitness, and program size.",

}

Genetic Programming entries for Tomasz Pawlak Krzysztof Krawiec

Citations