research-article

On relationships between semantic diversity, complexity and modularity of programming tasks

Author:

Krzysztof KrawiecAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 783 - 790

https://doi.org/10.1145/2330163.2330272

Published: 07 July 2012 Publication History

Abstract

We investigate semantic properties of linear programs, both internally, by analyzing the memory states they produce during execution, and externally, by inspecting program outcomes. The main concept of the formalism we propose is program trace, which reflects the behavior of program in semantic space. It allows us to characterize programming tasks in terms of traces of programs that solve them, and to propose certain measures that reveal their properties. We are primarily interested in measures that quantitatively characterize functional (semantic, behavioral) modularity of programming tasks. The experiments conducted on large samples of linear programs written in Push demonstrate that semantic structure varies from task to task, and reveal patterns of different forms of modularity. In particular, we identify interesting relationships between task modularity, task complexity, and program length, and conclude that a great share of programming tasks are modular.

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Cited By

La Cava WMoore J(2020)Learning feature spaces for regression with genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-020-09383-421:3(433-467)Online publication date: 11-Mar-2020
https://dl.acm.org/doi/10.1007/s10710-020-09383-4
La Cava WMoore JAuger AStützle T(2019)Semantic variation operators for multidimensional genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321776(1056-1064)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3321707.3321776
Krawiec KSwan JO’Reilly U(2016)Behavioral Program Synthesis: Insights and ProspectsGenetic Programming Theory and Practice XIII10.1007/978-3-319-34223-8_10(169-183)Online publication date: 22-Dec-2016
https://doi.org/10.1007/978-3-319-34223-8_10
Show More Cited By

Index Terms

On relationships between semantic diversity, complexity and modularity of programming tasks
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2012

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GECCO '12

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

La Cava WMoore J(2020)Learning feature spaces for regression with genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-020-09383-421:3(433-467)Online publication date: 11-Mar-2020
https://dl.acm.org/doi/10.1007/s10710-020-09383-4
La Cava WMoore JAuger AStützle T(2019)Semantic variation operators for multidimensional genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321776(1056-1064)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3321707.3321776
Krawiec KSwan JO’Reilly U(2016)Behavioral Program Synthesis: Insights and ProspectsGenetic Programming Theory and Practice XIII10.1007/978-3-319-34223-8_10(169-183)Online publication date: 22-Dec-2016
https://doi.org/10.1007/978-3-319-34223-8_10
Pawlak TWieloch BKrawiec K(2015)Semantic Backpropagation for Designing Search Operators in Genetic ProgrammingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.232125919:3(326-340)Online publication date: Jun-2015
https://doi.org/10.1109/TEVC.2014.2321259
Krawiec K(2014)Genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-013-9200-215:1(75-77)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s10710-013-9200-2
Krawiec KSwan JAlba E(2013)Pattern-guided genetic programmingProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463496(949-956)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463496

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