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A comparative study of an evolvability indicator and a predictor of expected performance for genetic programming

Published: 07 July 2012 Publication History

Abstract

An open question within Genetic Programming (GP) is how to characterize problemdifficulty. The goal is to develop predictive tools that estimate how difficult a problemis for GP to solve. Here we consider two groups of methods. We call the first group Evolvability Indicators (EI), measures that capture how amendable the fitness landscape is to a GP search. Examples of EIs are Fitness Distance Correlation (FDC) and Negative Slope Coefficient (NSC). The second group are Predictors of Expected Performance (PEP), models that take as input a set of descriptive attributes of a problem and predict the expected performance of GP. This paper compares an EI, the NSC, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of the GP classifiers. Conversely, the PEP models show a high correlation with GP performance.

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  • (2023)Models to classify the difficulty of genetic algorithms to solve continuous optimization problemsNatural Computing: an international journal10.1007/s11047-022-09936-923:2(431-451)Online publication date: 12-Jan-2023
  • (2016)Prediction of expected performance for a genetic programming classifierGenetic Programming and Evolvable Machines10.1007/s10710-016-9265-917:4(409-449)Online publication date: 1-Dec-2016
  • (2016)Predicting the RCGA Performance for the University Course Timetabling ProblemIntelligent Computing Systems10.1007/978-3-319-30447-2_3(31-45)Online publication date: 5-Mar-2016
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cover image ACM Conferences
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
July 2012
1586 pages
ISBN:9781450311786
DOI:10.1145/2330784

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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Author Tags

  1. classification
  2. genetic programming
  3. performance prediction

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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA

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View all
  • (2023)Models to classify the difficulty of genetic algorithms to solve continuous optimization problemsNatural Computing: an international journal10.1007/s11047-022-09936-923:2(431-451)Online publication date: 12-Jan-2023
  • (2016)Prediction of expected performance for a genetic programming classifierGenetic Programming and Evolvable Machines10.1007/s10710-016-9265-917:4(409-449)Online publication date: 1-Dec-2016
  • (2016)Predicting the RCGA Performance for the University Course Timetabling ProblemIntelligent Computing Systems10.1007/978-3-319-30447-2_3(31-45)Online publication date: 5-Mar-2016
  • (2014)Performance Classification of Genetic Algorithms on Continuous Optimization ProblemsNature-Inspired Computation and Machine Learning10.1007/978-3-319-13650-9_1(1-12)Online publication date: 2014

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