research-article

Introducing graphical models to analyze genetic programming dynamics

Authors:

Constantin Berzan,

Kalyan Veeramachaneni,

Una-May O'ReillyAuthors Info & Claims

FOGA XII '13: Proceedings of the twelfth workshop on Foundations of genetic algorithms XII

Pages 75 - 86

https://doi.org/10.1145/2460239.2460247

Published: 16 January 2013 Publication History

Abstract

We propose graphical models as a new means of understanding genetic programming dynamics. Herein, we describe how to build an unbiased graphical model from a population of genetic programming trees. Graphical models both express information about the conditional dependency relations among a set of random variables and they support probabilistic inference regarding the likelihood of a random variable's outcome. We focus on the former information: by their structure, graphical models reveal structural dependencies between the nodes of genetic programming trees. We identify graphical model properties of potential interest in this regard - edge quantity and dependency among nodes expressed in terms of family relations. Using a simple symbolic regression problem we generate a graphical model of the population each generation. Then we interpret the graphical models with respect to conventional knowledge about the influence of subtree crossover and mutation upon tree structure.

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Cited By

Iqbal MBrowne WZhang M(2015)Improving genetic search in XCS-based classifier systems through understanding the evolvability of classifier rulesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1369-719:7(1863-1880)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1007/s00500-014-1369-7
Song BLi VArnold D(2014)Gaussian mixture model of evolutionary algorithmsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598252(1423-1430)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598252

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cover image ACM Conferences

FOGA XII '13: Proceedings of the twelfth workshop on Foundations of genetic algorithms XII

January 2013

198 pages

ISBN:9781450319904

DOI:10.1145/2460239

General Chairs:
Frank Neumann
The University of Adelaide, Australia
,
Kenneth De Jong
George Mason University, USA

Copyright © 2013 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 16 January 2013

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FOGA '13

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FOGA '13: Foundations of Genetic Algorithms XII

January 16 - 20, 2013

Adelaide, Australia

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Overall Acceptance Rate 72 of 131 submissions, 55%

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Cited By

Iqbal MBrowne WZhang M(2015)Improving genetic search in XCS-based classifier systems through understanding the evolvability of classifier rulesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1369-719:7(1863-1880)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1007/s00500-014-1369-7
Song BLi VArnold D(2014)Gaussian mixture model of evolutionary algorithmsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598252(1423-1430)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598252

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