Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment: Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA

Geneticalgorithms(GAs)areapopulation-basedmeta-heuristicglobaloptimizationtechniquefor dealingwithcomplexproblemswithaverylargesearchspace.Thepopulationinitializationisacrucial taskinGAsbecauseitplaysavitalroleintheconvergencespeed,problemsearchspaceexploration, andalsothequalityofthefinaloptimalsolution.Thoughtheimportanceofdecidingproblem-specific populationinitializationinGAiswidelyrecognized,itishardlyaddressedintheliterature.Inthis article,differentpopulationseedingtechniquesforpermutation-codedgeneticalgorithmssuchas random,nearestneighbor(NN),genebank(GB),sortedpopulation(SP),andselectiveinitialization (SI),alongwiththreenewlyproposedordered-distance-vector-basedinitializationtechniqueshave beenextensivelystudied.Theabilityofeachpopulationseedingtechniquehasbeenexaminedin termsofasetofperformancecriteria,suchascomputationtime,convergencerate,errorrate,average convergence,convergencediversity,nearest-neighborratio,averagedistinctsolutionsanddistribution ofindividuals.Oneofthefamouscombinatorialhardproblemsofthetravelingsalesmanproblem (TSP)isbeingchosenasthetestbedandtheexperimentsareperformedonlarge-sizedbenchmark TSPinstancesobtainedfromstandardTSPLIB.Thescopeoftheexperimentsinthisarticleislimited totheinitializationphaseoftheGAandthisrestrictedscopehelpstoassesstheperformanceofthe populationseedingtechniquesintheirintendedphasealone.Theexperimentationanalysesarecarried outusingstatisticaltoolstoclaimtheuniqueperformancecharacteristicofeachpopulationseeding techniquesandbestperformingtechniquesareidentifiedbasedontheassessmentcriteriadefined andthenatureoftheapplication. KEywoRdS Combinatorial Problem, Genetic Algorithm, Order Distance Vector, Population Seeding Technique, Traveling Salesman Problem, Tsplib International Journal of Applied Metaheuristic Computing Volume 10 • Issue 2 • April-June 2019

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