abstract

Function optimization using cartesian genetic programming

Authors:

Julian F. Miller,

Maktuba MohidAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 147 - 148

https://doi.org/10.1145/2464576.2464646

Published: 06 July 2013 Publication History

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Abstract

In function optimization one tries to find a vector of real numbers that optimizes a complex multi-modal fitness function. Although evolutionary algorithms have been used extensively to solve such problems, genetic programming has not. In this paper, we show how Cartesian Genetic Programming can be readily applied to such problems. The technique can successfully find many optima in a standard suite of benchmark functions. The work opens up new avenues of research in the application of genetic programming and also offers an extensive set of highly developed benchmarks that could be used to compare the effectiveness of different GP methodologies.

References

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Cited By

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Bremer JLehnhoff S(2024)Hybridizing Lévy Flights and Cartesian Genetic Programming for Learning Swarm-Based OptimizationAdvances in Computational Intelligence Systems10.1007/978-3-031-47508-5_24(299-310)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-3-031-47508-5_24
Bremer JLehnhoff S(2023)Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by CMA-ESSystems10.3390/systems1104017711:4(177)Online publication date: 28-Mar-2023
https://doi.org/10.3390/systems11040177
Manazir ARaza K(2019)Recent Developments in Cartesian Genetic Programming and its VariantsACM Computing Surveys10.1145/327551851:6(1-29)Online publication date: 28-Jan-2019
https://dl.acm.org/doi/10.1145/3275518
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Function optimization using cartesian genetic programming

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Published In

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Conference

GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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

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Cited By

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Bremer JLehnhoff S(2024)Hybridizing Lévy Flights and Cartesian Genetic Programming for Learning Swarm-Based OptimizationAdvances in Computational Intelligence Systems10.1007/978-3-031-47508-5_24(299-310)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-3-031-47508-5_24
Bremer JLehnhoff S(2023)Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by CMA-ESSystems10.3390/systems1104017711:4(177)Online publication date: 28-Mar-2023
https://doi.org/10.3390/systems11040177
Manazir ARaza K(2019)Recent Developments in Cartesian Genetic Programming and its VariantsACM Computing Surveys10.1145/327551851:6(1-29)Online publication date: 28-Jan-2019
https://dl.acm.org/doi/10.1145/3275518
Póvoa RKoshiyama ADias DSouza PHorta B(2019)Unimodal optimization using a genetic-programming-based method with periodic boundary conditionsGenetic Programming and Evolvable Machines10.1007/s10710-019-09373-1Online publication date: 17-Dec-2019
https://doi.org/10.1007/s10710-019-09373-1
Miller J(2019)Cartesian genetic programming: its status and futureGenetic Programming and Evolvable Machines10.1007/s10710-019-09360-6Online publication date: 6-Aug-2019
https://doi.org/10.1007/s10710-019-09360-6
Vissol-Gaudin EKotsialos AMassey MGroves CPearson CZeze DPetty M(2017)Solving binary classification problems with carbon nanotube / liquid crystal composites and evolutionary algorithms2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969536(1924-1931)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969536
Qaiser FKotsialos AMassey MZeze DPearson CPetty M(2016)Manipulating the conductance of single-walled carbon nanotubes based thin films for evolving threshold logic circuits using particle swarm optimisation2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7748357(5255-5261)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7748357
Mohid MMiller JHarding STufte GMassey MPetty M(2016)Evolution-in-materioSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1928-620:8(3007-3022)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s00500-015-1928-6
Vissol-Gaudin EKotsialos AMassey MZeze DPearson CGroves CPetty M(2016)Data Classification Using Carbon-Nanotubes and Evolutionary AlgorithmsParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_60(644-654)Online publication date: 31-Aug-2016
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Vissol-Gaudin EKotsialos AMassey MZeze DPearson CGroves CPetty M(2016)Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary AlgorithmsProceedings of the 15th International Conference on Unconventional Computation and Natural Computation - Volume 972610.1007/978-3-319-41312-9_11(130-141)Online publication date: 11-Jul-2016
https://dl.acm.org/doi/10.1007/978-3-319-41312-9_11
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