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Functional modularity for genetic programming

Published: 08 July 2009 Publication History

Abstract

In this paper we introduce, formalize, and experimentally validate a novel concept of functional modularity for Genetic Programming (GP). We rely on module definition that is most natural for GP: a piece of program code (subtree). However, as opposed to syntax-based approaches that abstract from the actual computation performed by a module, we analyze also its semantic using a set of fitness cases. In particular, the central notion of this approach is subgoal, an entity that embodies module's desired semantic and is used to evaluate module candidates. As the cardinality of the space of all subgoals is exponential with respect to the number of fitness cases, we introduce monotonicity to assess subgoals' potential utility for searching for good modules. For a given subgoal and a sample of modules, monotonicity measures the correlation of subgoal's distance from module's semantics and the fitness of the solution the module is part of. In the experimental part we demonstrate how these concepts may be used to describe and quantify the modularity of two simple problems of Boolean function synthesis. In particular, we conclude that monotonicity usefully differentiates two problems with different nature of modularity, allows us to tell apart the useful subgoals from the other ones, and may be potentially used for problem decomposition and enhance the efficiency of evolutionary search.

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cover image ACM Conferences
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
July 2009
2036 pages
ISBN:9781605583259
DOI:10.1145/1569901
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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Published: 08 July 2009

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Author Tags

  1. genetic programming
  2. modularity
  3. problem decomposition

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GECCO09
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GECCO09: Genetic and Evolutionary Computation Conference
July 8 - 12, 2009
Québec, Montreal, Canada

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  • (2021)Tag-based regulation of modules in genetic programming improves context-dependent problem solvingGenetic Programming and Evolvable Machines10.1007/s10710-021-09406-8Online publication date: 7-Jul-2021
  • (2021)Getting a Head Start on Program Synthesis with Genetic ProgrammingGenetic Programming10.1007/978-3-030-72812-0_17(263-279)Online publication date: 25-Mar-2021
  • (2019)Modularity metrics for genetic programmingProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326908(2056-2059)Online publication date: 13-Jul-2019
  • (2013)Locally geometric semantic crossoverGenetic Programming and Evolvable Machines10.1007/s10710-012-9172-714:1(31-63)Online publication date: 1-Mar-2013
  • (2012)Comparing methods for module identification in grammatical evolutionProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330277(823-830)Online publication date: 7-Jul-2012
  • (2011)A non-destructive grammar modification approach to modularity in grammatical evolutionProceedings of the 13th annual conference on Genetic and evolutionary computation10.1145/2001576.2001766(1411-1418)Online publication date: 12-Jul-2011
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  • (2010)An examination on the modularity of grammars in grammatical evolutionary designIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586483(1-8)Online publication date: Jul-2010
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