EVOLVINGBUILDINGBLOCKSFORDESIGNUSINGGENETIC ENGINEERING A FO(7)

Ifwecommencebytreatingtheproblemasoneof ndingimproveddesignsthenfromacomputationalviewpointthisformofevolutionspeedsuptheconver-

(intermsofthenumberofgenerationsgencetoimproveddesignsbyupto

required)whencomparedwithstandardgeneticalgorithms.Itappearsthattheuseofatargetedrepresentationcanleadtotheproductionofdesignswhicharelocallyoptimal.

However,ifweusethecompletionevolutionapproachpresentedinthesecondexperimentwegetfurtherimprovementsinperformance.WewillleavetotheDis-cussionsectionfurtherdiscussionoftheotheradvantagesoftheapproachpresen-ted.

5.Discussion

Theanalysisjustpresentedcanbeeasilyextendedtoincludegeneralobjectgram-marsoftypesdifferenttothekindergartengrammar.Theproposedapproachcanbeconsideredasanimplementationofthesimplestversionofthegeneticengin-eeringapproachtothegenericdesignproblem.Fromthetechnicalpointofviewthealgorithmpresentedisamixtureofastochasticsearchmethod(whichmaybeageneticalgorithm)andapatternrecognitiontechnique.

Thegeneticengineeringapproachcanbeappliedinasimilarfashiontotheproblemofthegenerationofa“suitable”shapegrammar(GeroandKazakov,1995)wherethecomplexbuildingblockscorrespondtotheevolvedgrammarrules.

Abstract. This paper presents a formal approach to the evolution of a representation for use in a design process. The approach adopted is based on concepts associated with genetic engineering. An initial set of genes representing elementary building blocks

18JohnS.GeroANDVladimirA.Kazakov

Asalreadymentionedintheanalysisofthenumericalexperiment,theevolvedrepresentationsarehighlyredundant-thesamecompositebuildingblocksareevolvedmanytimesalongthedifferentbranchesoftheevolutionarytrees.Theredundancylevelofthecurrentsetofcompositebuildingblockscanbereducedinanumberofdifferentways.Thesimplestisjusttodeletealltheredundantcopiesfromthecurrentset.Inthegeneralcase,wehaveto ndtheminimalrepresentationofthesubspacewhichcanbegeneratedusingthecurrentsetofcomplexbuildingblocks.Theintroductionofideasandmethodsfromgeneticengineeringintodesignsystemsbasedongeneticalgorithmsopensupanumberofavenuesforresearchintobothevolutionary-baseddesignsynthesisandintomodi edgeneticalgorithms.Indesignsystemsbasedonsuchmodi edgeneticalgorithmsitispossibletocon-sidertwodirections.

The rstistotreatthesequenceofthegeneswhichresultsincertainbehavioursor tnessperformancesasaformof‘emergence’,emergenceoftheschemarepres-entedbythatgenesequence.Theuseofthegeneticallyengineeredcomplexgeneschangesthepropertiesovertimeofthestatespaceswhicharebeingsearched.Thisallowsustoconsidertheprocessasbeingrelatedtodesignexplorationmodelledinaclosedworld.Theprecisemannerinwhichtheprobabilitiesassociatedwithstatesinthestatespacechangeisnotyetknown.Clearly,thisisalsoafunctionofwhethera xedlengthgenotypeencodingisusedornot.Ifavariablelengthgenotypeencodingisusedwiththegeneticallyengineeredcomplexgenesthentheshapeofthestatespaceremains xedbuttheprobabilitiesassociatedwiththestateswithinitchange.Ifa xedlengthgenotypeencodingisusedwiththege-neticallyengineeredcomplexgenesthentheshapeofthestatespacechangesinadditiontotheprobabilitiesassociatedwithstatesinthestatespace.

Thesecondistotreatthegeneticallyengineeredcomplexgenesasameansofdevelopingarepresentationforpotentialdesigns.Afundamentalpartofdesign-ingisthedeterminationofanappropriaterepresentationofthecomponentswhichareusedinthestructure(Gero,1990)ofthedesign.Thisispartofthataspectofdesigningcalled‘formulation’,iethedeterminationofthevariables,theirre-lationshipsandthecriteriabywhichresultingdesignswillbeevaluated.Inmostcomputer-aideddesignsystemsthecomponentsmapdirectlyontovariables.Fur-ther,insuchsystemsthevariablesarespeci edattheoutset,asaconsequencethereisanunspeci edmappingbetweenthesolutionscapableofbeingproducedandthevariableschosentorepresenttheideaswhicharetobecontainedintheresultingdesigns.Thegeneticengineeringapproachdescribedprovidesameansofautomat-ingtherepresentationpartoftheformulationprocess.Thelevelofgranularityisdeterminedbythestabilityconditionoftheevolutionaryprocessorcanbedeterm-inedbytheuser.Thetargetedbuildingblocksprovideahigh-levelstartingpointforalllaterdesignswhicharetoexhibittherequiredcharacteristicsasevidencedintheearlierdesigns.Itisthislatterrequirementwhichismetbythisformalmethod.Thefollowingsimplepicturecanbeusedtosummarizethemodeldescribedin

Abstract. This paper presents a formal approach to the evolution of a representation for use in a design process. The approach adopted is based on concepts associated with genetic engineering. An initial set of genes representing elementary building blocks

EvolvingBuildingBlocksforDesignUsingGeneticEngineering19thispaper.Agroupofchildrenareplayingwiththe“Lego”gameusingnotmorethan50squares.Theyjointhemtogetherandwanttobuildtheobjectwiththelargestnumberofclosedspacesinside.Aftereachchildhasbuilthisorherob-jectthesupervisortriesto ndacombinationofsquareswhichispresentinmanyofthebestdesignsbutispresentinnoneoronlyinafewofunsatisfactorydesigns.Thenhemakesthiscombinationpermanentbygluingitscomponentstogetherandaddsabunchofsuchpermanentcombinationstothepoolofbuildingelementsavailabletothechildren.Thenthechildrenmakeanothersetofobjectsusingthesenewbuildingblocksaswellasanoldones.Thesupervisortriesto ndanother“good”compositeblockandtheprocessisrepeated.Thus,twostepsoccurineachcycle: rstchildrenmakeasetofnewdesignsfromcurrentlyavailableblocksandcombinationofblocksandsecondthesupervisortriestosingleouttheadditionalcombinationofblocksthatshouldbeemployed.Iftherearenosuchcombinationswhichdistinguish“good”designfromthe“bad”onesthenwewillnotgetnewcombinationsbutonlytheimproveddesigns.

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