research-article

MT-CGP: mixed type cartesian genetic programming

Authors:

Vincent Graziano,

Jürgen Leitner,

Jürgen SchmidhuberAuthors Info & Claims

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

Pages 751 - 758

https://doi.org/10.1145/2330163.2330268

Published: 07 July 2012 Publication History

Abstract

The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of Cartesian Genetic Programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks.

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Cited By

De La Torre CCortacero KCussat-Blanc SWilson DLi XHandl J(2024)Multimodal Adaptive Graph EvolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654347(499-502)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654347
De La Torre CLavinas YCortacero KLuga HWilson DCussat-Blanc S(2024)Multimodal Adaptive Graph Evolution for Program SynthesisParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_19(306-321)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_19
Biau JCussat-Blanc SLuga H(2024)Improving Image Filter Efficiency: A Multi-objective Genetic Algorithm Approach to Optimize Computing EfficiencyApplications of Evolutionary Computation10.1007/978-3-031-56852-7_2(19-34)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_2
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MT-CGP: mixed type cartesian genetic programming
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

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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New York, NY, United States

Publication History

Published: 07 July 2012

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GECCO '12

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GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Cited By

De La Torre CCortacero KCussat-Blanc SWilson DLi XHandl J(2024)Multimodal Adaptive Graph EvolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654347(499-502)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654347
De La Torre CLavinas YCortacero KLuga HWilson DCussat-Blanc S(2024)Multimodal Adaptive Graph Evolution for Program SynthesisParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_19(306-321)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70055-2_19
Biau JCussat-Blanc SLuga H(2024)Improving Image Filter Efficiency: A Multi-objective Genetic Algorithm Approach to Optimize Computing EfficiencyApplications of Evolutionary Computation10.1007/978-3-031-56852-7_2(19-34)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_2
Cortacero KMcKenzie BMüller SKhazen RLafouresse FCorsaut GVan Acker NFrenois FLamant LMeyer NVergier BWilson DLuga HStaufer ODustin MValitutti SCussat-Blanc S(2023)Evolutionary design of explainable algorithms for biomedical image segmentationNature Communications10.1038/s41467-023-42664-x14:1Online publication date: 6-Nov-2023
https://doi.org/10.1038/s41467-023-42664-x
Cui HMargraf AHähner J(2022)Refining Mutation Variants in Cartesian Genetic ProgrammingBioinspired Optimization Methods and Their Applications10.1007/978-3-031-21094-5_14(185-200)Online publication date: 10-Nov-2022
https://doi.org/10.1007/978-3-031-21094-5_14
Bremer J(2022)Learning to OptimizeRecent Advances in Computational Optimization10.1007/978-3-031-06839-3_1(1-19)Online publication date: 17-Sep-2022
https://doi.org/10.1007/978-3-031-06839-3_1
Manazir ARaza K(2022)Comparative Evaluation of Genetic Operators in Cartesian Genetic ProgrammingIntelligent Systems Design and Applications10.1007/978-3-030-96308-8_71(765-774)Online publication date: 27-Mar-2022
https://doi.org/10.1007/978-3-030-96308-8_71
Milano NNolfi S(2020)Enhancing Cartesian genetic programming through preferential selection of larger solutionsEvolutionary Intelligence10.1007/s12065-020-00421-9Online publication date: 17-May-2020
https://doi.org/10.1007/s12065-020-00421-9
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
Wong WLease B(2019)Spherical Bounding Classifier using CGP Generated TransformsIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/495/1/012016495(012016)Online publication date: 7-Jun-2019
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