Learning Classifier Systems: 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

Lanzi, Pier Luca [Editor]; Stolzmann, Wolfgang [Editor]; Wilson, Stewart W. [Editor];

ISBN 10: 3540205446 / ISBN 13: 9783540205449

Published by Springer, 2004

About this Item

We are unable to ship to Canada at this time.Ex-library paperback in very nice condition with the usual markings and attachments. Text block clean and unmarked. Tight binding.

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Bibliographic Details

• Title: Learning Classifier Systems: 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
• Publisher: Springer
• Publication Date: 2004
• Language: English
• ISBN 10: 3540205446
• ISBN 13: 9783540205449
• Binding: Paperback
• Condition: Very Good
• Weight: 13 ounces

About this title

Synopsis

The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7–8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.