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A new crossover technique for Cartesian genetic programming

Published: 07 July 2007 Publication History

Abstract

Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    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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    Published: 07 July 2007

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    Author Tags

    1. cartesian genetic programming
    2. crossover techniques
    3. optimisation

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (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
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