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020 _a9783540400295
_9978-3-540-40029-5
024 7 _a10.1007/b94229
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aLearning Classifier Systems
_h[electronic resource] :
_b5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers /
_cedited by Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.
250 _a1st ed. 2003.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2003.
300 _aVII, 233 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2661
505 0 _aBalancing Specificity and Generality in a Panmictic-Based Rule-Discovery Learning Classifier System -- A Ruleset Reduction Algorithm for the XCS Learning Classifier System -- Adapted Pittsburgh-Style Classifier-System: Case-Study -- The Effect of Missing Data on Learning Classifier System Learning Rate and Classification Performance -- XCS’s Strength-Based Twin: Part I -- XCS’s Strength-Based Twin: Part II -- Further Comparison between ATNoSFERES and XCSM -- Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection -- Anticipatory Classifier System Using Behavioral Sequences in Non-Markov Environments -- Mapping Artificial Immune Systems into Learning Classifier Systems -- The 2003 Learning Classifier Systems Bibliography.
520 _aThe 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.
650 0 _aArtificial intelligence.
650 0 _aComputer science.
650 0 _aMachine theory.
650 0 _aDatabase management.
650 1 4 _aArtificial Intelligence.
650 2 4 _aTheory of Computation.
650 2 4 _aFormal Languages and Automata Theory.
650 2 4 _aDatabase Management.
700 1 _aLanzi, Pier Luca.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aStolzmann, Wolfgang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWilson, Stewart W.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540205449
776 0 8 _iPrinted edition:
_z9783662172568
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2661
856 4 0 _uhttps://doi.org/10.1007/b94229
912 _aZDB-2-SCS
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