Article

Evolutionary tree genetic programming

Authors:

Ján Antolík,

William H. HsuAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 1789 - 1790

https://doi.org/10.1145/1068009.1068312

Published: 25 June 2005 Publication History

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Abstract

We introduce a clustering-based method of subpopulation management in genetic programming (GP) called Evolutionary Tree Genetic Programming (ETGP). The biological motivation behind this work is the observation that the natural evolution follows a tree-like phylogenetic pattern. Our goal is to simulate similar behavior in artificial evolutionary systems such as GP. To test our model we use three common GP benchmarks: the Ant Algorithm, 11-Multiplexer, and Parity problems.The performance of the ETGP system is empirically compared to those of the GP system. Code size and variance are consistently reduced by a small but statistically significant percentage, resulting in a slight speedup in the Ant and 11-Multiplexer problems, while the same comparisons on the Parity problem are inconclusive.

References

[1]

J. Antolík. Evolutionary tree genetic programming. Master's thesis, Computing and Information Sciences department, Kansas State University, Apr. 2004.

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[2]

S. Baluja. A massively distributed parallel genetic algorithm, 1992.

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[3]

S. Luke. Evolutionary computation in java. http://www.cs.umd.edu/projects/plus/ec/ecj/, 2001.

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Cited By

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Gasmi KKharrat AMessaoud MAbid M(2012)Automated segmentation of brain tumor using optimal texture features and support vector machine classifierProceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II10.1007/978-3-642-31298-4_28(230-239)Online publication date: 25-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-31298-4_28
Kouchakpour PZaknich ABräunl T(2008)A survey and taxonomy of performance improvement of canonical genetic programmingKnowledge and Information Systems10.1007/s10115-008-0184-921:1(1-39)Online publication date: 12-Dec-2008
https://doi.org/10.1007/s10115-008-0184-9

Index Terms

Evolutionary tree genetic programming
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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Published In

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

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genetic programming

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GECCO05

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

Washington DC, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Gasmi KKharrat AMessaoud MAbid M(2012)Automated segmentation of brain tumor using optimal texture features and support vector machine classifierProceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II10.1007/978-3-642-31298-4_28(230-239)Online publication date: 25-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-31298-4_28
Kouchakpour PZaknich ABräunl T(2008)A survey and taxonomy of performance improvement of canonical genetic programmingKnowledge and Information Systems10.1007/s10115-008-0184-921:1(1-39)Online publication date: 12-Dec-2008
https://doi.org/10.1007/s10115-008-0184-9

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