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Combining cartesian genetic programming with an estimation of distribution algorithm

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Janet CleggAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1333 - 1334

https://doi.org/10.1145/1389095.1389350

Published: 12 July 2008 Publication History

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Combining cartesian genetic programming with an estimation of distribution algorithm

Pages 1333 - 1334

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Abstract

This paper describes initial testing of a novel idea to combine a CGP with an EDA. In recent work a new improved crossover technique was successfully applied to a CGP. To implement the new method meant changing the traditional CGP representation. The new representation developed in that work lends itself very nicely to some probability distribution being implemented. The work in this paper has investigated this idea of incoporating estimated probability distributions into the new CGP method with crossover.

References

[1]

J. Clegg, J.A. Walker, J.F. Miller. A new crossover technique for Cartesian Genetic Programming . In Proceedings of the 2007 Genetic and Evolutionary Computation Conference, pages 1580--1587, London, 2007.

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[2]

J. F. Miller and P. Thomson. Cartesian genetic programming. In Proceedings of the 3rd European Conference on Genetic Programming (EuroGP 2000), volume 1802 of Lecture Notes in Computer Science, pages 121--132, Edinburgh, 2000. Springer-Verlag.

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A. Ratle and M. Sebag. Avoiding the bloat with stochastic grammer-based genetic programming. In Artificial Evolution - Lecture notes in computer science vol 2310, pages 255--266, Springer 2002.

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R. Salustowicz and J. Schmidhuber. Probabilistic incremental program evolution. In Evolutionary Computation, (5) pages 123--141, 1997.

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Cited By

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Loebl JRozinajová V(2019)Continuous Cartesian Genetic Programming with Particle Swarm OptimizationIntelligent Systems Design and Applications10.1007/978-3-030-16660-1_96(985-995)Online publication date: 14-Apr-2019
https://doi.org/10.1007/978-3-030-16660-1_96
Kim KMcKay RHoai N(2016)Recursion-Based Biases in Stochastic Grammar Model Genetic ProgrammingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.242542020:1(81-95)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1109/TEVC.2015.2425420
Kim KShan YNguyen XMckay R(2014)Probabilistic model building in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-013-9205-x15:2(115-167)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1007/s10710-013-9205-x

Index Terms

Combining cartesian genetic programming with an estimation of distribution algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Published: 12 July 2008

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July 12 - 16, 2008

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Loebl JRozinajová V(2019)Continuous Cartesian Genetic Programming with Particle Swarm OptimizationIntelligent Systems Design and Applications10.1007/978-3-030-16660-1_96(985-995)Online publication date: 14-Apr-2019
https://doi.org/10.1007/978-3-030-16660-1_96
Kim KMcKay RHoai N(2016)Recursion-Based Biases in Stochastic Grammar Model Genetic ProgrammingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.242542020:1(81-95)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1109/TEVC.2015.2425420
Kim KShan YNguyen XMckay R(2014)Probabilistic model building in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-013-9205-x15:2(115-167)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1007/s10710-013-9205-x

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A new crossover technique for Cartesian genetic programming

Solving real-valued optimisation problems using cartesian genetic programming

A comparison of Cartesian genetic programming and linear genetic programming

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