Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis
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- @Article{Pawlak:cgsgp:EC,
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author = "Tomasz P. Pawlak and Krzysztof Krawiec",
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title = "Competent Geometric Semantic Genetic Programming for
Symbolic Regression and Boolean Function Synthesis",
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journal = "Evolutionary Computation",
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year = "2018",
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volume = "26",
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number = "2",
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pages = "177--212",
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month = "Summer",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/EVCO_a_00205",
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size = "37 pages",
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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