Genetic and Non-Genetic Operators in ALECSYS

Author:

Dorigo Marco1

Affiliation:

1. International Computer Science Institute, 1947 Center Street, Suite 600, Berkeley, California 94704-1105 USA

Abstract

It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficulty in regulating interplay between the reward system and the background genetic algorithm (GA), rule chains' instability, default hierarchies' instability, among others. ALECSYS is a parallel version of a standard learning classifier system (CS) and, as such, suffers from these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec is a new genetic operator used to specialize potentially useful classifiers. Energy is a quantity introduced to measure global convergence to apply the genetic algorithm only when the system is close to a steady state. Dynamic adjustment of the classifiers set cardinality speeds up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source.

Publisher

MIT Press - Journals

Subject

Computational Mathematics

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2. On the Effects of Absumption for XCS with Continuous-Valued Inputs;Applications of Evolutionary Computation;2021

3. Foreword;Acta Commercii;2017-01-09

4. Learning Classifier Systems: A Complete Introduction, Review, and Roadmap;Journal of Artificial Evolution and Applications;2009-09-22

5. Combining latent learning with dynamic programming in the modular anticipatory classifier system;European Journal of Operational Research;2005-02

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