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Pattern-guided genetic programming

Published: 06 July 2013 Publication History

Abstract

Online progress in search and optimization is often hindered by neutrality in the fitness landscape, when many genotypes map to the same fitness value. We propose a method for imposing a gradient on the fitness function of a metaheuristic (in this case, Genetic Programming) via a metric (Minimum Description Length) induced from patterns detected in the trajectory of program execution. These patterns are induced via a decision tree classifier. We apply this method to a range of integer and boolean-valued problems, significantly outperforming the standard approach. The method is conceptually straightforward and applicable to virtually any metaheuristic that can be appropriately instrumented.

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cover image ACM Conferences
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
July 2013
1672 pages
ISBN:9781450319638
DOI:10.1145/2463372
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 July 2013

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Author Tags

  1. genetic programming
  2. mdl
  3. neutrality
  4. program trace
  5. push

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  • Research-article

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GECCO '13
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GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2020)Program Synthesis in a Continuous Space Using Grammars and Variational AutoencodersParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58115-2_3(33-47)Online publication date: 2-Sep-2020
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  • (2018)Semantic schema modeling for genetic programming using clustering of building blocksApplied Intelligence10.1007/s10489-017-1052-748:6(1442-1460)Online publication date: 1-Jun-2018
  • (2018)Exploiting Subprograms in Genetic ProgrammingGenetic Programming Theory and Practice XV10.1007/978-3-319-90512-9_1(1-16)Online publication date: 6-Jul-2018
  • (2017)Online discovery of search objectives for test-based problemsEvolutionary Computation10.1162/evco_a_0017925:3(375-406)Online publication date: 1-Sep-2017
  • (2017)Exploring Fitness and Edit Distance of Mutated Python ProgramsGenetic Programming10.1007/978-3-319-55696-3_2(19-34)Online publication date: 15-Mar-2017
  • (2016)Online Discovery of Search Objectives for Test-based ProblemsProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2930954(163-164)Online publication date: 20-Jul-2016
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