Article

Limiting the Number of Fitness Cases in Genetic Programming Using Statistics

Authors:

Mario Giacobini,

Marco Tomassini,

Leonardo VanneschiAuthors Info & Claims

PPSN VII: Proceedings of the 7th International Conference on Parallel Problem Solving from Nature

Pages 371 - 380

Published: 07 September 2002 Publication History

Abstract

Fitness evaluation is often a time consuming activity in genetic programming applications and it is thus of interest to find criteria that can help in reducing the time without compromising the quality of the results. We use well-known results in statistics and information theory to limit the number of fitness cases that are needed for reliable function reconstruction in genetic programming. By using two numerical examples, we show that the results agree with our theoretical predictions. Since our approach is problem-independent, it can be used together with techniques for choosing an efficient set of fitness cases.

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Cited By

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Downey CZhang MLiu J(2012)Parallel linear genetic programming for multi-class classificationGenetic Programming and Evolvable Machines10.1007/s10710-012-9162-913:3(275-304)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1007/s10710-012-9162-9
Santosa HMilton JKennedy PChu WWong WPalakal MHung C(2011)HMXT-GPProceedings of the 2011 ACM Symposium on Applied Computing10.1145/1982185.1982420(1070-1075)Online publication date: 21-Mar-2011
https://dl.acm.org/doi/10.1145/1982185.1982420
Li GZeng X(2010)Bottom-Up tree evaluation in tree-based genetic programmingProceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I10.1007/978-3-642-13495-1_63(513-522)Online publication date: 12-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13495-1_63
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PPSN VII: Proceedings of the 7th International Conference on Parallel Problem Solving from Nature

September 2002

941 pages

ISBN:3540441395

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Springer-Verlag

Berlin, Heidelberg

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Published: 07 September 2002

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Downey CZhang MLiu J(2012)Parallel linear genetic programming for multi-class classificationGenetic Programming and Evolvable Machines10.1007/s10710-012-9162-913:3(275-304)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1007/s10710-012-9162-9
Santosa HMilton JKennedy PChu WWong WPalakal MHung C(2011)HMXT-GPProceedings of the 2011 ACM Symposium on Applied Computing10.1145/1982185.1982420(1070-1075)Online publication date: 21-Mar-2011
https://dl.acm.org/doi/10.1145/1982185.1982420
Li GZeng X(2010)Bottom-Up tree evaluation in tree-based genetic programmingProceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I10.1007/978-3-642-13495-1_63(513-522)Online publication date: 12-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13495-1_63
Xie HZhang MAndreae P(2006)Population clustering in genetic programmingProceedings of the 9th European conference on Genetic Programming10.1007/11729976_17(190-201)Online publication date: 10-Apr-2006
https://dl.acm.org/doi/10.1007/11729976_17
Ziegler JBanzhaf W(2003)Decreasing the number of evaluations in evolutionary algorithms by using a meta-model of the fitness functionProceedings of the 6th European conference on Genetic programming10.5555/1762668.1762693(264-275)Online publication date: 14-Apr-2003
https://dl.acm.org/doi/10.5555/1762668.1762693

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