research-article

Redundancy and computational efficiency in Cartesian genetic programming

Authors:

J. F. Miller,

S. L. SmithAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 10, Issue 2

Pages 167 - 174

https://doi.org/10.1109/TEVC.2006.871253

Published: 01 September 2006 Publication History

Abstract

The graph-based Cartesian genetic programming system has an unusual genotype representation with a number of advantageous properties. It has a form of redundancy whose role has received little attention in the published literature. The representation has genes that can be activated or deactivated by mutation operators during evolution. It has been demonstrated that this "junk" has a useful role and is very beneficial in evolutionary search. The results presented demonstrate the role of mutation and genotype length in the evolvability of the representation. It is found that the most evolvable representations occur when the genotype is extremely large and in which over 95% of the genes are inactive.

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Kalkreuth RBaeck TLi XHandl J(2024)CGP++ : A Modern C++ Implementation of Cartesian Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654092(13-22)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654092
Prashanth HRao M(2024)Accelerated and Highly Correlated ASIC Synthesis of AI Hardware Subsystems Using CGPIET Computers & Digital Techniques10.1049/2024/66236372024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1049/2024/6623637
Huang ZMei YZhang FZhang MBanzhaf W(2024)Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic schedulingGenetic Programming and Evolvable Machines10.1007/s10710-023-09478-825:1Online publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1007/s10710-023-09478-8
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Redundancy and computational efficiency in Cartesian genetic programming
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 10, Issue 2

April 2006

105 pages

ISSN:1089-778X

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IEEE Press

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Published: 01 September 2006

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Kalkreuth RBaeck TLi XHandl J(2024)CGP++ : A Modern C++ Implementation of Cartesian Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654092(13-22)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654092
Prashanth HRao M(2024)Accelerated and Highly Correlated ASIC Synthesis of AI Hardware Subsystems Using CGPIET Computers & Digital Techniques10.1049/2024/66236372024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1049/2024/6623637
Huang ZMei YZhang FZhang MBanzhaf W(2024)Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic schedulingGenetic Programming and Evolvable Machines10.1007/s10710-023-09478-825:1Online publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1007/s10710-023-09478-8
Cui HHeider MHähner J(2024)Positional Bias Does Not Influence Cartesian Genetic Programming with CrossoverParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_10(151-167)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70055-2_10
Virgolin MBosman PWagner M(2022)Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regressionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534036(2289-2297)Online publication date: 9-Jul-2022
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Lapid RSipper MWagner M(2022)Evolution of activation functions for deep learning-based image classificationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533949(2113-2121)Online publication date: 9-Jul-2022
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LaKemper CWang CYoder JWagner M(2022)Biology inspired growth in meta-learningProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533945(63-64)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533945
Kalkreuth RSotto LVašíček ZWagner M(2022)Graph-based genetic programmingProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533657(958-982)Online publication date: 9-Jul-2022
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Shi XMinku LYao X(2022)A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuitGenetic Programming and Evolvable Machines10.1007/s10710-022-09436-w23:4(453-493)Online publication date: 1-Dec-2022
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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: 17-Nov-2022
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Effects of code growth and parsimony pressure on populations in genetic programming

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