Pier L. Lanzi - Böcker

Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.

Evolutionary Computation (EC) is a rapidly expanding eld of computer science in which problem solving, optimization, and machine learning techniques ins- red by genetics and natural selection are studied. In recent years, a number of studies and results have been reported in the literature which have disclosed the potentials of EC techniques and shown their capability to solve hard problems in several domains. This volume contains the proceedings of EvoWorkshops 2001, an event - cluding the First European Workshop on Evolutionary Computation in Com- natorial Optimization (EvoCOP), the Second European Workshop on Evolut- nary Aeronautics (EvoFlight), the Third European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP), the First - ropean Workshop on Evolutionary Learning (EvoLearn), and the Second Eu- pean Workshop on Evolutionary Scheduling and Timetabling (EvoSTIM). These workshops were held in Como, Italy, on 18 and 19 April 2001, as part of - roGP 2001, the Fourth European Conference on Genetic Programming.EvoCOP focuses on applications of evolutionary algorithms and related h- ristic search methods to various combinatorial optimization problems. It also covers general methodological aspects of such algorithms like operator analyses, search dynamics, tness landscapes, and algorithmic comparisons, which are the driving force in gaining a better understanding of evolutionary search and hence support the design of e ective evolutionary algorithms for combinatorial op- mization problems of practical relevance. EvoFlight is aimed at bringing together researchers and industrial parties to discuss the use of evolutionary computation in aerospace.

This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Learning Classifier Systems, IWLCS 2000, held in Paris, France in September 2000. The 13 revised full papers presented have gone through two rounds of reviewing and selection. Also included is a comprehensive LCS bibliography listing more than 600 entries as well as an appendix. The papers are organized in topical sections on theory, applications, and advanced architectures.

The Fourth International Workshop on Learning Classi?er Systems (IWLCS 2001)washeldJuly7-8,2001,inSanFrancisco,California,duringtheGenetic andEvolutionaryComputationConference(GECCO2001). Wehaveincluded inthisvolumerevisedandextendedversionsofelevenofthepaperspresented attheworkshop. Thevolumeisorganizedintotwomainparts. The?rstisdedicatedtoimportant theoreticalissuesoflearningclassi?ersystemsresearchincludingthein?uence ofexplorationstrategy,amodelofself-adaptiveclassi?ersystems,andtheuse ofclassi?ersystemsforsocialsimulation. Thesecondpartcontainspapersd- cussing applications of learning classi?er systems such as data mining, stock trading,andpowerdistributionnetworks. AnappendixcontainsapaperpresentingaformaldescriptionofACS,arapidly emerginglearningclassi?ersystemmodel. Thisbookistheidealcontinuationofthetwovolumesfromthepreviouswo- shops,publishedbySpringer-VerlagasLNAI1813andLNAI1996. Wehopeit willbeausefulsupportforresearchersinterestedinlearningclassi?ersystems andwillprovideinsightsintothemostrelevanttopicsandthemostinteresting openissues. April2002 PierLucaLanzi WolfgangStolzmann StewartW. Wilson Organization The Fourth International Workshop on Learning Classi?er Systems (IWLCS 2001)washeldJuly7-8,2001inSanFrancisco(CA),USA,duringtheGenetic andEvolutionaryConference(GECCO2001).OrganizingCommittee PierLucaLanzi PolitecnicodiMilano,Italy WolfgangStolzmann DaimlerChryslerAG,Germany StewartW. Wilson TheUniversityofIllinoisatUrbana-Champaign,USA PredictionDynamics,USA ProgramCommittee ErikBaum NECResearchInstitute,USA AndreaBonarini PolitecnicodiMilano,Italy LashonB. Booker TheMITRECorporation,USA MartinV. Butz UniversityofWur .. zburg,Germany LawrenceDavis NuTechSolutions,USA TerryFogarty SouthbankUniversity,UK JohnH. Holmes UniversityofPennsylvania,USA TimKovacs UniversityofBirmingham,UK PierLucaLanzi PolitecnicodiMilano,Italy RickL. Riolo UniversityofMichigan,USA OlivierSigaud AnimatLab-LIP6,France RobertE. Smith TheUniversityofTheWestofEngland,UK WolfgangStolzmann DaimlerChryslerAG,Germany KeikiTakadama ATRInternational,Japan StewartW. Wilson TheUniversityofIllinoisatUrbana-Champaign,USA PredictionDynamics,USA TableofContents ITheory BiasingExplorationinanAnticipatoryLearningClassi?erSystem ...3 MartinV. Butz An Incremental Multiplexer Problem and Its Uses in Classi?er System Research...23 LawrenceDavis,ChunshengFu,StewartW. Wilson AMinimalModelofCommunicationforaMulti-agentClassi?erSystem. .32 ' GillesEn'ee,CathyEscazut A Representation for Accuracy-Based Assessment of Classi?er System PredictionPerformance...43 JohnH. Holmes ASelf-AdaptiveXCS...57 JacobHurst,LarryBull TwoViewsofClassi?erSystems ...74 TimKovacs SocialSimulationUsingaMulti-agentModelBasedonClassi?erSystems: TheEmergenceofVacillatingBehaviourinthe"ElFarol"BarProblem...88 LuisMiramontesHercog,TerenceC. Fogarty II Applications XCSandGALE:AComparativeStudyofTwoLearningClassi?erSystems onDataMining...115 EsterBernad'o,XavierLlor'a,JosepM. Garrell APreliminaryInvestigationofModi?edXCSasaGenericDataMining Tool...133 PhillipWilliamDixon,DavidW. Corne,MartinJohnOates ExplorationsinLCSModelsofStockTrading ...151 SoniaSchulenburg,PeterRoss On-LineApproachforLossReductioninElectricPowerDistribution NetworksUsingLearningClassi?erSystems...181 Patr'?ciaAmancioVargas,ChristianoLyraFilho, FernandoJ. VonZuben VIII TableofContents CompactRulesetsfromXCSI ...197 StewartW. Wilson III Appendix AnAlgorithmicDescriptionofACS2 ...211 MartinV. Butz,WolfgangStolzmann AuthorIndex ...231 BiasingExplorationinan AnticipatoryLearningClassi?erSystem MartinV. Butz DepartmentofCognitivePsychology,UniversityofWurz .. burg R.. ontgenring11,97070Wurz .. burg,Germany butz@psychologie. uni-wuerzburg. de Abstract. Thechapterinvestigateshowmodelandbehaviorallearning can be improved in an anticipatory learning classi?er system by bi- ing exploration. First, theappliedsystemACS2isexplained. Next,an overviewoverthepossibilitiesofapplyingexplorationbiasesinanant- ipatory learning classi?er systemand speci?cally ACS2 is provided.

This book constitutes the refereed proceedings of six workshops on evolutionary computation held concurrently as EvoWorkshops 2000 in Edinburgh, Scotland, UK, in April 2000. The 37 revised papers presented were carefully reviewed and selected by the respective program committees. All in all, the book demonstrates the broad application potential of evolutionary computing in a variety of fields. In accordance with the individual workshops, the book is divided into sections on image and signal processing; systems, controls, and drives in industry; telecommunications; scheduling and timetabling; robotics; and aeronautics.