Title: Pittsburgh-style learning classifier system for multiple environments: towards robust waterbus route for several situations

Authors: Keiji Sato; Keiki Takadama

Addresses: Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan. ' Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan; PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan

Abstract: This paper proposes an accuracy-based Pittsburgh-style learning classifier system (LCS) that can find effective and robust solutions against several different situations, and aims at investigating its effectiveness in the waterbus route optimisation problem. For this purpose, our accuracy-based Pittsburgh-style LCS: 1) introduces a new fitness calculation to remain robust classifiers (i.e., solutions) in different situations 2) employs NSGA-II to find the most effective and robust solutions among a lot of Pareto front solutions found in the multi-objective optimisation. Through intensive simulations on the waterbus route optimisation problem, we have revealed that our proposed LCS can find the waterbus routes that can cope with two different situations. In detail: 1) the relative fitness calculation can find the robust routes in comparison with the ordinary fitness calculation 2) the accuracy-based selection of the parents succeeds to find more effective and robust route in the different environments in comparison with the NSGA-II-based selection in the multi-objective optimisation.

Keywords: learning classifier systems; LCS; accuracy; multi-objective optimisation; nondominated sorting genetic algorithms; NSGA-II; waterbus route optimisation; robust classifiers.

DOI: 10.1504/IJBIC.2011.043609

International Journal of Bio-Inspired Computation, 2011 Vol.3 No.6, pp.370 - 383

Available online: 07 Nov 2011 *

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