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1、EvolutionaryComputationinConstraintSatisfactionandMachineLearningJanoI.vanHemertLeidenInstituteofAdvancedComputerScience,LeidenUniversityjvhemert@cs.leidenuniv.nl1IntroductionAtfirstsightthetwoproblemareasconstraintsatisfactionandmachinelearningdonotappeartobesimilar.Thefi
2、rsthasacleardefinitionofitsproblemdomainandacrispdefinitionofsolutions.Thesecondisamuchbroaderdefinedproblemdomain,whichleadstomanyobjectivestobesolved.Manyresearchareashavefocusedthereattentiononconstraintsatisfaction,operatingresearch,antcolonies,evolutionarycomputationan
3、d,mostnotable,constraintprogramming.Althoughtheproblemsthatarebeingstudiedsharethesamegoal,whichistosatisfyasetofconstraints,theirprecisedefinitionvaries.Amongtheseproblemswefindnumerouswellknownonessuchas,k-graphcolouring,3-satandn-queens.Foranyoftheseproblemswecantransfo
4、rmthemtoabinaryconstraintsatisfactionproblemwithoutlossofgenerality.Thisholdsforanyfiniteconstraintsatisfactionproblem,anditmeansthatwecansolveaproblembysolvingacorrespondingbinaryrepresentation[14],whichmeansthateveryconstraintonlyrestrictstheassignmentofvaluestotwovaria
5、bles.Inpractice,however,weneedtobecarefulasdifferentformsoftransformationarepossible,eachwithitsownimpactontheperformanceoftheappliedsolvingalgorithm.Incontrasttothefieldofconstraintsatisfactionthefieldofmachinelearn-ingtriestocopewithmanydifferentaspects.Therewefindproblemss
6、uchasclustering,classification,associationrulesandsymbolicregression.Oftentheseareliftedoutoneatatimeresultinginspecifictechniquestosolvethem.Itisuncommontofindaclassifyingtechniquethisiscapableofclusteringtoo.TechniquesfromEvolutionaryComputationmaychangethispractice.Basic
7、allymachinelearningisoneofthelaststepsintheprocessofdatamining.Itisthemomentwherewetransformrawdataintoknowledge,whichmakesitanimportantstepasthisformsthemainideabehinddatamining.Herewefocusonthoseproblemsfromthetwoareasthathavebeensolvedwithevolutionarycomputation.Ashor
8、toverviewofthesefollowsnext.There-afterwegiveanoverviewoftheempiricalresearchwehaveperformedoverthepast