Learning Classifier Systems | 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers

Pier Luca Lanzi (u. a.)

ISBN 10: 3540205446 / ISBN 13: 9783540205449

Published by Springer, 2003

About this Item

Learning Classifier Systems | 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers | Pier Luca Lanzi (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2003 | Springer | EAN 9783540205449 | Verantwortliche Person für die EU: Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg, productsafety[at]springernature[dot]com | Anbieter: preigu.

Seller Inventory # 102480345

Bibliographic Details

• Title: Learning Classifier Systems | 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers
• Publisher: Springer
• Publication Date: 2003
• Language: English
• ISBN 10: 3540205446
• ISBN 13: 9783540205449
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 14D millimeters 235 millimeters width by 155 millimeters height by 14 millimeters depth
• Weight: 376 grams
• Seller catalogs:
  • Bücher

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.