research-article

Using feature-based fitness evaluation in symbolic regression with added noise

Authors:

Janine H. Imada,

Brian J. RossAuthors Info & Claims

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

Pages 2153 - 2158

https://doi.org/10.1145/1388969.1389039

Published: 12 July 2008 Publication History

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Abstract

Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the dependent variable directly calculated from the candidate expression. While this approach is extremely successful in many instances, its performance can deteriorate in the presence of noise. In this paper, a feature-based fitness function is considered, in which the fitness scores are determined by comparing the statistical features of the sequence of values, rather than the actual values themselves. The set of features used in the fitness evaluation are customized according to the target, and are drawn from a wide set of features capable of characterizing a variety of behaviours. Experiments examining the performance of the feature-based and standard fitness functions are carried out for non-oscillating and oscillating targets in a GP system which introduces noise during the evaluation of candidate expressions. Results show strength in the feature-based fitness function, especially for the oscillating target.

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Cited By

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Miranda LOliveira LMartins JPappa GBosman P(2017)How noisy data affects geometric semantic genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071300(985-992)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071300
Zarnegar AVamplew PStranieri A(2009)Inference of gene expression networks using memetic gene expression programmingProceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 9110.5555/1862659.1862666(29-36)Online publication date: 1-Jan-2009
https://dl.acm.org/doi/10.5555/1862659.1862666
Ross BImada JRothlauf F(2009)Evolving stochastic processes using feature tests and genetic programmingProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570044(1059-1066)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1570044
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Index Terms

Using feature-based fitness evaluation in symbolic regression with added noise
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 '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

July 2008

1182 pages

ISBN:9781605581316

DOI:10.1145/1388969

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

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Association for Computing Machinery

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Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Cited By

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Miranda LOliveira LMartins JPappa GBosman P(2017)How noisy data affects geometric semantic genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071300(985-992)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071300
Zarnegar AVamplew PStranieri A(2009)Inference of gene expression networks using memetic gene expression programmingProceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 9110.5555/1862659.1862666(29-36)Online publication date: 1-Jan-2009
https://dl.acm.org/doi/10.5555/1862659.1862666
Ross BImada JRothlauf F(2009)Evolving stochastic processes using feature tests and genetic programmingProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570044(1059-1066)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1570044
Ross BImada J(2009)Using Multi-Objective Genetic Programming to Synthesize Stochastic ProcessesGenetic Programming Theory and Practice VII10.1007/978-1-4419-1626-6_10(159-175)Online publication date: 20-Oct-2009
https://doi.org/10.1007/978-1-4419-1626-6_10

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