Analysis and improvement of fitness exploitation in XCS: bounding models, tournament selection, and bilateral accuracy

  • Authors:

  • Martin V. Butz;David E. Goldberg;Kurian Tharakunnel

  • Affiliations:

  • Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign, Urbana, IL;Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign, Urbana, IL;Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign, Urbana, IL

  • Venue:
  • Evolutionary Computation
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

The evolutionary learning mechanism in XCS strongly depends on its accuracy-based fitness approach. The approach is meant to result in an evolutionary drive from classifiers of low accuracy to those of high accuracy. Since, given inaccuracy, lower specificity often corresponds to lower accuracy, fitness pressure most often also results in a pressure towards higher specificity. Moreover, fitness pressure should cause the evolutionary process to be innovative in that it combines low-order building blocks of lower accurate classifiers, to higher-order building blocks with higher accuracy. This paper investigates how, when, and where accuracy-based fitness results in successful rule evolution in XCS. Along the way, a weakness in the current proportionate selection method in XCS is identified. Several problem bounds are derived that need to be obeyed to enable proper evolutionary pressure. Moreover, a fitness dilemma is identified that causes accuracy-based fitness to be misleading. Improvements are introduced to XCS to make fitness pressure more robust and overcome the fitness dilemma. Specifically, (1) tournament selection results in a much better fitness-bias exploitation, and (2) bilateral accuracy prevents the fitness dilemma. While the improvements stand for themselves, we believe they also contribute to the ultimate goal of an evolutionary learning system that is able to solve decomposable machine-learning problems quickly, accurately, and reliably. The paper also contributes to the further understanding of XCS in general and the fitness approach in XCS in particular.