Genetic algorithms using multi-objectives(10)

We are interested in a job-shop scheduling problem corresponding to an industrial problem. Gantt diagram’s optimization can be considered as an NP-difficult problem. Determining an optimal solution is almost impossible, but trying to improve the current s

188A.Cardonetal./RoboticsandAutonomousSystems33(2000)

179–190

Fig.8.Evolutionoftheeconomicfunctionaccordingtothenumberofagentsandthenumberofgenetic

operations.

causedbythepossibleexposuretoexternaleventsoriginatingintheenvironmentandduringthegeneticcodereplicationphase.

Ifwealsointroducethenotionofmotivatedbe-haviorforagents[5],wegodeeplyintoarti ciallifeproblematics.Thegeneticautonomyandthenotionofmotivationforanagentmayleadtoadrasticallynewkindofemergencephenomenon[1](differentso-cialbehavior,auto-referringevaluationprocess,etc.)inself-organizingMASs.Itiscertainlyadif culttaskbutitmaysowtheseedsofaproli capproachcon-cerningarti ciallife.10.Conclusion

Determininganoptimalsolutionisalmostimpos-sible,buttryingtoimproveanexistingsolutionisthewaytoimprovetaskallocation.Duringthesimulationprocess,agentsgranularityappearswiththemutationbehaviorintroducedbyGA[38].Attheendofthesimulation,communicationsbetweenglobalandlocal

Fig.9.MASandGAintheenvironment.