poster

A comparison between geometric semantic GP and cartesian GP for boolean functions learning

Authors:

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 143 - 144

https://doi.org/10.1145/2598394.2598475

Published: 12 July 2014 Publication History

Get Access

Abstract

Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.

References

[1]

A. Mambrini, L. Manzoni, and A. Moraglio. Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions. In Foundations of Genetic Algorithm - FOGA 2013, pages 119--132, Adelaide, Australia, January 2013. ACM.

Digital Library

Google Scholar

[2]

J. McDermott, D. R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, W. Jaskowski, K. Krawiec, R. Harper, K. De Jong, and U.-M. O'Reilly. Genetic programming needs better benchmarks. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pages 791--798, New York, NY, USA, 2012. ACM.

Digital Library

Google Scholar

[3]

J. F. Miller. Cartesian genetic programming. Springer, 2011.

Crossref

Google Scholar

[4]

J. F. Miller and S. L. Smith. Redundancy and computational efficiency in cartesian genetic programming. IEEE Transactions on Evolutionary Computation, 10(2):167--174, April 2006.

Digital Library

Google Scholar

[5]

A. Moraglio, K. Krawiec, and C. Johnson. Geometric semantic genetic programming. In PPSN '12, volume 7491 of Lecture Notes in Computer Science, pages 21--31. Springer, 2012.

Digital Library

Google Scholar

[6]

F. Neumann and C. Witt. Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Springer, 2010.

Crossref

Google Scholar

[7]

D. White, J. McDermott, M. Castelli, L. Manzoni, B. Goldman, G. Kronberger, W. Jaskowski, U. O'Reilly, and S. Luke. Better gp benchmarks: community survey results and proposals. Genetic Programming and Evolvable Machines, 14(1):3--29, 2013.

Digital Library

Google Scholar

[8]

D. A. Wolfe and M. Hollander. Nonparametric statistical methods. John Wiley New York, 1973.

Google Scholar

Cited By

View all

Corus DOliveto P(2024)On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block MutationsACM Transactions on Evolutionary Learning and Optimization10.1145/36771244:4(1-33)Online publication date: 27-Nov-2024
https://dl.acm.org/doi/10.1145/3677124
Kötzing TLagodzinski JLengler JMelnichenko A(2018)Destructiveness of Lexicographic Parsimony Pressure and Alleviation by a Concatenation Crossover in Genetic ProgrammingParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99259-4_4(42-54)Online publication date: 21-Aug-2018
https://doi.org/10.1007/978-3-319-99259-4_4
Doerr BKötzing TLagodzinski JLengler JBosman P(2017)Bounding bloat in genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071271(921-928)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071271
Show More Cited By

Index Terms

A comparison between geometric semantic GP and cartesian GP for boolean functions learning
1. Computing methodologies
  1. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
2. Theory of computation
  1. Design and analysis of algorithms

Recommendations

Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions
FOGA XII '13: Proceedings of the twelfth workshop on Foundations of genetic algorithms XII

Geometric Semantic Genetic Programming (GSGP) is a recently introduced form of Genetic Programming (GP), rooted in a geometric theory of representations, that searches directly the semantic space of functions/programs, rather than the space of their ...
An Investigation of Geometric Semantic GP with Linear Scaling
GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference

Geometric semantic genetic programming (GSGP) and linear scaling (LS) have both, independently, shown the ability to outperform standard genetic programming (GP) for symbolic regression. GSGP uses geometric semantic genetic operators, different from ...
HMXT-GP: an information-theoretic approach to genetic programming that maintains diversity
SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

This paper applies a recent information--theoretic approach to controlling Genetic Algorithms (GAs) called HMXT to tree--based Genetic Programming (GP). HMXT, in a GA domain, requires the setting of selection thresholds in a population and the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

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.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

Check for updates

Author Tags

Qualifiers

Poster

Conference

GECCO '14

Sponsor:

SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

Acceptance Rates

GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
70
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 09 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Corus DOliveto P(2024)On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block MutationsACM Transactions on Evolutionary Learning and Optimization10.1145/36771244:4(1-33)Online publication date: 27-Nov-2024
https://dl.acm.org/doi/10.1145/3677124
Kötzing TLagodzinski JLengler JMelnichenko A(2018)Destructiveness of Lexicographic Parsimony Pressure and Alleviation by a Concatenation Crossover in Genetic ProgrammingParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99259-4_4(42-54)Online publication date: 21-Aug-2018
https://doi.org/10.1007/978-3-319-99259-4_4
Doerr BKötzing TLagodzinski JLengler JBosman P(2017)Bounding bloat in genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071271(921-928)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071271
Nguyen QPham TNguyen XMcdermott J(2016)Subtree semantic geometric crossover for genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-015-9253-517:1(25-53)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s10710-015-9253-5

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions

An Investigation of Geometric Semantic GP with Linear Scaling

HMXT-GP: an information-theoretic approach to genetic programming that maintains diversity

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations