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Introduction to Genetic Algorithms · S.N.Sivanandam S.N.Deepa Introduction to Genetic Algorithms With 193 Figures and 13 Tables Authors S.N.Sivanandam S.N.Deepa ProfessorandHead Ph.DScholarDept.ofComputerScience Dept.ofComputerScienceandEngineering andEngineering PSGCollegeofTechnology PSGCollegeofTechnology Coimbatore-641004 Coimbatore-641004 TN,India TN,India LibraryofCongressControlNumber:2007930221 ISBN978-3-540-73189-4SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broadcasting, reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9, 1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violations areliableforprosecutionundertheGermanCopyrightLaw. SpringerisapartofSpringerScience+BusinessMedia springer.com (cid:2)c Springer-VerlagBerlinHeidelberg2008 Theuseofgeneral descriptive names,registered names,trademarks, etc. inthis publication does not imply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotective lawsandregulationsandthereforefreeforgeneraluse. Typesetting:IntegraSoftwareServicesPvt.Ltd.,India Coverdesign:ErichKirchner,Heidelberg Printedonacid-freepaper SPIN:12053230 89/3180/Integra 5 4 3 2 1 0 Preface Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are notcompletelyunderstood(evennowadays),thereexistsomepointssupportedby strongexperimentalevidence: • Evolutionisaprocessoperatingoverchromosomesratherthanoverorganisms. Theformerareorganictoolsencodingthestructureofalivingbeing,i.e.,acrea- tureis“built”decodingasetofchromosomes. • Naturalselectionisthemechanismthatrelateschromosomeswiththeefficiency oftheentitytheyrepresent,thusallowingthatefficientorganismwhichiswell- adaptedtotheenvironmenttoreproducemoreoftenthanthosewhicharenot. • Theevolutionaryprocesstakesplaceduringthereproductionstage.Thereexists a large number of reproductivemechanisms in Nature. Most common onesare mutation (that causes the chromosomes of offspring to be different to those of the parents)and recombination(thatcombinesthe chromosomesof the parents toproducetheoffspring). Based upon the features above,the three mentioned models of evolutionarycom- putingwereindependently(andalmostsimultaneously)developed. An EvolutionaryAlgorithm(EA) is an iterative and stochastic processthatop- erates on a set of individuals (population). Each individual represents a potential solution to the problembeing solved.This solution is obtainedby meansof a en- coding/decodingmechanism. Initially, the population is randomly generated (per- haps with the help of a constructionheuristic). Everyindividualin the population isassigned,bymeansofafitnessfunction,ameasureofitsgoodnesswithrespect to the problem under consideration.This value is the quantitative informationthe algorithmusestoguidethesearch. Amongthe evolutionarytechniques,the geneticalgorithms(GAs) are themost extendedgroupofmethodsrepresentingtheapplicationofevolutionarytools.They rely on the use of a selection, crossover and mutation operators. Replacement is usuallybygenerationsofnewindividuals. IntuitivelyaGAproceedsbycreatingsuccessivegenerationsofbetterandbetter individuals by applying very simple operations. The search is only guided by the fitness value associated to every individual in the population. This value is used to rank individuals depending on their relative suitability for the problem being v vi Preface solved. The problem is the fitness function that for every individual is encharged ofassigningthefitnessvalue. The location of this kind of techniques with respect to other deterministic and non-deterministicproceduresisshowninthefollowingtree.Thisfigurebelowout- linesthesituationofnaturaltechniquesamongotherwell-knownsearchprocedures. Combinations of EAs with Hill-Climbing algorithms are very powerful. Ge- neticalgorithmsintensivelyusingsuchlocalsearchmechanismaretermedMemetic Algorithms. Also parallelmodelsincrease the extensionandquality of the search. TheEAsexplorationcomparesquitewellagainsttherestofsearchtechniquesfor a similar search effort. Exploitation is a more difficult goal in EAs but nowadays manysolutionsexistforEAstorefinesolutions. Genetic algorithms are currently the most prominent and widely used compu- tational models of evolution in artificial-life systems. These decentralized models provideabasisforunderstandingmanyothersystemsandphenomenaintheworld. ResearchesonGAsinalifegiveillustrativeexamplesinwhichthegeneticalgorithm isusedtostudyhowlearningandevolutioninteract,andtomodelecosystems,im- munesystem,cognitivesystems,andsocialsystems. About the Book This book is meant for a wide range of readers, who wishes to learn the basic conceptsofGeneticAlgorithms.Itcanalsobemeantforprogrammers,researchers andmanagementexpertswhoseworkisbasedonoptimizationtechniques.Theba- sicconceptsofGeneticAlgorithmsaredealtindetailwiththerelevantinformation and knowledge available for understanding the optimization process. The various operators involved for Genetic Algorithm operation are explained with examples. Theadvancedoperatorsandthevariousclassificationshavebeendiscussedinlucid manner,sothatastartercanunderstandtheconceptswithaminimaleffort. ThesolutionstospecificproblemsaresolvedusingMATLAB7.0andthesolu- tionsaregiven.TheMATLABGAtoolboxhasalsobeenincludedforeasyreference ofthe readersso thattheycan havehandsonworkingwith variousGA functions. ApartfromMATLABsolutions,certainproblemsarealsosolvedusingCandC++ andthesolutionsaregiven. Thebookisdesignedtogiveabroadin-depthknowledgeonGeneticAlgorithm. This book can be used as a handbookand a guide for students of all engineering disciplines, management sector, operational research area, computer applications, andforvariousprofessionalswhoworkinOptimizationarea. GeneticAlgorithms,atpresent,is a hottopic amongacademicians,researchers andprogramdevelopers.Duetowhich,thisbookisnotonlyforstudents,butalso forawiderangeofresearchersanddeveloperswhoworkinthisfield.Thisbookcan be used as a ready referenceguidefor Genetic Algorithmresearch scholars.Most oftheoperators,classificationsandapplicationsforawidevarietyofareascovered herefulfillsasanadvancedacademictextbook. To conclude,we hope that the reader will find this book a helpfulguide and a valuablesourceofinformationaboutGeneticAlgorithmconceptsfortheirseveral practicalapplications. 1 Organizationofthe Book The book contains11 chaptersaltogether. It starts with the introductionto Evolu- tionaryComputing.Thevariousapplicationcasestudiesarealsodiscussed. Thechaptersareorganizedasfollows: vii viii AbouttheBook • Chapter1givesanintroductiontoEvolutionarycomputing,itsdevelopmentand itsfeatures. • Chapter 2 enhancesthe growthof Genetic Algorithmsand its comparisonwith otherconventionaloptimizationtechniques.Alsothebasicsimplegeneticalgo- rithmwithitsadvantagesandlimitationsarediscussed. • Thevariousterminologiesandthebasicoperatorsinvolvedingeneticalgorithm aredealtinChap.3.Fewexampleproblems,enablingthereaderstounderstand thebasicgeneticalgorithmoperationarealsoincluded. • Chapter 4 discusses the advanced operatorsand techniquesinvolvedin genetic algorithm. • Thedifferentclassificationsofgeneticalgorithmareprovidedin Chap.5.Each oftheclassificationsis discussedwiththeiroperatorsandmodeofoperationto achieveoptimizedsolution. • Chapter6givesabriefintroductiontogeneticprogramming.Thestepsinvolved and characteristics of genetic programming with its applications are described here. • Chapter 7 discusses on variousgenetic algorithm optimizationproblemswhich includes fuzzy optimization, multi objective optimization, combinatorial optimization,schedulingproblemsandsoon. • TheimplementationofgeneticalgorithmusingMATLABisdiscussedinChap.8. Thetoolboxfunctionsandsimulatedresultstospecificproblemsareprovidedin thischapter. • Chapter 9 gives the implementation of genetic algorithm concept using C and C++.Theimplementationisperformedforfewbenchmarkproblems. • Theapplicationofgeneticalgorithminvariousemergingfieldsalongwithcase studiesisgiveninChapter10. • Chapter 11 gives a brief introduction to particle swarm optimization and ant colonyoptimization. TheBibliographyisgivenattheendforthereadyreferenceofreaders. 2 SalientFeatures ofthe Book Thesalientfeaturesofthebookinclude: • DetailedexplanationofGeneticAlgorithmconcepts • NumerousGeneticAlgorithmOptimizationProblems • StudyonvarioustypesofGeneticAlgorithms • ImplementationofOptimizationproblemusingCandC++ • SimulatedsolutionsforGeneticAlgorithmproblemsusingMATLAB7.0 • BriefdescriptiononthebasicsofGeneticProgramming • ApplicationcasestudiesonGeneticAlgorithmonemergingfields S.N. Sivanandam completed his B.E (Electrical and Electronics Engineering) in 1964fromGovernmentCollegeofTechnology,CoimbatoreandM.Sc(Engineering) AbouttheBook ix in Power System in 1966 from PSG College of Technology, Coimbatore. He acquiredPhDinControlSystemsin1982fromMadrasUniversity.Hehasreceived Best Teacher Award in the year 2001 and Dhakshina Murthy Award for Teaching ExcellencefromPSGCollegeofTechnology.HereceivedTheCITATIONforbest teachingandtechnicalcontributionintheYear2002,GovernmentCollegeofTech- nology,Coimbatore.Hehasa totalteachingexperience(UGandPG) of41years. Thetotalnumberofundergraduateandpostgraduateprojectsguidedbyhimforboth Computer Science and Engineering and Electrical and Electronics Engineering is around 600. He is currently working as a Professor and Head Computer Science andEngineeringDepartment,PSGCollegeofTechnology,Coimbatore[fromJune 2000]. He has been identified as an outstanding person in the field of Computer Science and Engineering in MARQUIS “Who’s Who”, October 2003 issue, New providence,NewJersey,USA.Hehasalsobeenidentifiedasanoutstandingperson inthefieldofComputationalScienceandEngineeringin“Who’sWho”,December 2005 issue, Saxe-CoburgPublications, United Kingdom.He has been placed as a VIPmemberinthecontinentalWHO’sWHORegistryofnationalBusinessLeaders, Inc.33WestHawthorneAvenueValleyStream,NY11580,Aug24,2006. S.N.Sivanandamhaspublished12 books.He hasdeliveredaround150special lectures of different specialization in Summer/Winter school and also in various Engineeringcolleges.He hasguidedandcoguided30Ph.D researchworksandat present9Ph.Dresearchscholarsareworkingunderhim.Thetotalnumberoftech- nicalpublicationsinInternational/Nationaljournals/Conferencesisaround700.He hasalsoreceivedCertificateofMerit2005–2006forhispaperfromTheInstitution of Engineers (India). He has chaired 7 International conferences and 30 National conferences.He is a memberof variousprofessionalbodieslike IE (India),ISTE, CSI,ACSandSSI.HeisatechnicaladvisorforvariousreputedindustriesandEn- gineeringInstitutions.HisresearchareasincludeModelingandSimulation,Neural networks , Fuzzy Systems and Genetic Algorithm, Pattern Recognition, Multi di- mensionalsystemanalysis,LinearandNonlinearcontrolsystem,SignalandImage processing,ControlSystem, Power system, Numericalmethods,Parallel Comput- ing,DataMiningandDatabaseSecurity. S.N.DeepahascompletedherB.EDegreefromGovernmentCollegeofTechnol- ogy,Coimbatore,1999and M.E DegreefromPSG College of Technology,Coim- batore, 2004. She was a gold medalist in her B.E Degree Programme. She has receivedG.DMemorialAwardintheyear1997andBestOutgoingStudentAward fromPSGCollegeofTechnology,2004.HerM.EThesiswonNationalAwardfrom the Indian Society of Technical Education and L&T, 2004. She has published 5 booksandpapersinInternationalandNationalJournals.Herresearchareasinclude Neural Network, Fuzzy Logic, Genetic Algorithm, Digital Control, Adaptive and Non-linearControl. Acknowledgement TheauthorsarealwaysthankfultotheAlmightyforperseveranceandachievements. They wish to thank Shri G. Rangaswamy, Managing Trustee, PSG Institutions, ShriC.R.Swaminathan,ChiefExecutive;andDr.R.Rudramoorthy,Principal,PSG CollegeofTechnology,Coimbatore,fortheirwhole-heartedcooperationandgreat encouragementgiveninthissuccessfulendeavor.Theyalsowishtothankthestaff membersofcomputerscienceandengineeringfortheircooperation.Deepawishes tothankherhusbandAnand,daughterNivethithaandparentsfortheirsupport. xi

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Combinations of EAs with Hill-Climbing algorithms are very powerful. Ge- netic algorithms . Neural Network, Fuzzy Logic, Genetic Algorithm, Digital Control, Adaptive and. Non-linear 9 Genetic Algorithm Optimization in C/C++ .