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

Authors:
Leonardo Trujillo;Yuliana Martínez;Edgar Galván López;Pierrick Legrand
Affiliations:
Instituto Tecnológico de Tijuana, Tijuana, Mexico;Instituto Tecnológico de Tijuana, Tijuana, Mexico;Trinity College Dublin, Dublin, Ireland;Université Victor Segalen Bordeaux 2, Bordeaux, France
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

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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.