skip to main content
10.1145/3583133.3590706acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

The Influence of Probabilistic Grammars on Evolution

Published: 24 July 2023 Publication History

Abstract

Context-Free Grammars (CFGs) are used in Genetic Programming (GP) to encode the structure and syntax of programs, enabling efficient exploration of potential solutions and generation of well-formed and syntactically correct programs. Probabilistic Context-Free Grammars (PCFG) can be used to model the distribution of solutions to help guide the search process. Structured Grammatical Evolution (SGE) is a grammar-based GP algorithm that uses a list of dynamic lists as its genotype, where each list represents the ordered indexes of production rules to expand for each non-terminal in the grammar. Two recent variants incorporate PCFG into the SGE framework, where the probabilities of the production rule change during the evolutionary process, resulting in improved performance.
This study examines the impact of these differences on the behavior of SGE and its variants, Probabilistic Structured Grammatical Evolution (PSGE) and Co-evolutionary Probabilistic Structured Grammatical Evolution (Co-PSGE), in terms of population tree depth, genotype size, new solutions generated, and runtime. The results indicate that the use of probabilistic alternatives can affect the growth of tree depth and size and increases the ability to generate new solutions.

References

[1]
K. Kim, Y. Shan, N. X. Hoai, and R. I. McKay. 2013. Probabilistic model building in genetic programming: a critical review. Genetic Programming and Evolvable Machines 15, 2 (Sept. 2013), 115--167.
[2]
J. R. Koza. 1994. Genetic programming as a means for programming computers by natural selection. Statistics and Computing 4, 2 (June 1994).
[3]
N. Lourenço, F. Assunção, F. B. Pereira, E. Costa, and P. Machado. 2018. Structured Grammatical Evolution: A Dynamic Approach. In Handbook of Grammatical Evolution. Springer International Publishing, 137--161.
[4]
N. Lourenço, J. Ferrer, F. B. Pereira, and E. Costa. 2017. A Comparative Study of Different Grammar-Based Genetic Programming Approaches. In Lecture Notes in Computer Science. Springer International Publishing, 311--325.
[5]
N. Lourenço, F. B. Pereira, and E. Costa. 2016. Unveiling the properties of structured grammatical evolution. Genetic Programming and Evolvable Machines 17, 3 (Feb. 2016), 251--289.
[6]
R. I. McKay, N. X. Hoai, P. A. Whigham, Y. Shan, and M. O'Neill. 2010. Grammar-based Genetic Programming: a survey. Genetic Programming and Evolvable Machines 11, 3--4 (May 2010), 365--396.
[7]
J. Mégane, N. Lourenço, and P. Machado. 2022. Co-evolutionary probabilistic structured grammatical evolution. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM.
[8]
J. Mégane, N. Lourenço, and P. Machado. 2022. Probabilistic Structured Grammatical Evolution. In 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE.
[9]
A. Ratle and M. Sebag. 2002. Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming. In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 255--266.
[10]
F. Rothlauf and D. E. Goldberg. 2003. Redundant Representations in Evolutionary Computation. Evolutionary Computation 11, 4 (Dec. 2003), 381--415.
[11]
C. Ryan, M. O'Neill, and J.J. Collins (Eds.). 2018. Handbook of Grammatical Evolution. Springer International Publishing.
[12]
R. Sałustowicz and J. Schmidhuber. 1997. Probabilistic Incremental Program Evolution: Stochastic search through program space. In Machine Learning: ECML-97. Springer Berlin Heidelberg, 213--220.
[13]
D. Schweim and F. Rothlauf. 2018. An Analysis of the Bias of Variation Operators of Estimation of Distribution Programming. In Proceedings of the Genetic and Evolutionary Computation Conference (Kyoto, Japan) (GECCO '18). Association for Computing Machinery, New York, NY, USA, 1191--1198.
[14]
D. Schweim, A. Thorhauer, and F. Rothlauf. 2018. On the Non-uniform Redundancy of Representations for Grammatical Evolution: The Influence of Grammars. Springer International Publishing, Cham.
[15]
A. Thorhauer and F. Rothlauf. 2014. On the Locality of Standard Search Operators in Grammatical Evolution. In Parallel Problem Solving from Nature - PPSN XIII. Springer International Publishing, 465--475.
[16]
P. A. Whigham, G. Dick, J. Maclaurin, and C. A. Owen. 2015. Examining the "Best of Both Worlds" of Grammatical Evolution. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. ACM.
[17]
P. Wong, L. Lo, M. Wong, and K. Leung. 2014. Grammar-Based Genetic Programming with Bayesian network. In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE.
[18]
Kohsuke Y. and Hitoshi I. 2004. Program Evolution by Integrating EDP and GP. In Genetic and Evolutionary Computation - GECCO 2004, Kalyanmoy Deb (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 774--785.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
July 2023
2519 pages
ISBN:9798400701207
DOI:10.1145/3583133
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2023

Check for updates

Author Tags

  1. grammatical evolution
  2. probabilistic

Qualifiers

  • Poster

Funding Sources

Conference

GECCO '23 Companion
Sponsor:

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 56
    Total Downloads
  • Downloads (Last 12 months)24
  • Downloads (Last 6 weeks)8
Reflects downloads up to 03 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media