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120 C. Gil, R. Baños, M. G. Montoya, A. Márquez, and J. Ortega4. Conclusions of the workIn this paper, we have proposed a new hybrid multiobjective evolutionary algorithm based onnon-dominated sorting approach of NSGA-II and internal and external archiving approach ofPESA. We have compared its performance with three others recent MOEAs on a suite of testfunction. As we have commented in the experimental results section, although an order ofmerit between different algorithms is very difficult, we have focused this work to obtain amore precise analysis about spreading of solutions. Comparative performance was measuredusing a coverage metric and we found that msPESA was able to maintain a better spread ofsolutions and convergence better in the obtained nondominated front. However, results ona limited set of test functions must always be regarded as tentative, and hence much furtherwork is needed.AcknowledgmentsThis work was supported by the Spanish MCyT under contracts TIC2002-00228. Authorsappreciate the support of the "Structuring the European Research Area" program, R113-CT-2003-506079, funded by the European Commission. R. Baños acknowledges a FPI doctoralfellowship from the regional government of Andalucia.References[1] Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, New York,1989.[2] Fonseca, C. M.; Flemming, P. J. Genetic Algorithms for Multiobjective Optimization: Formulation, Discusionand Generalization. In S. Forrest (eds.): Proceedings of the Fifth International Conference on Genetic Algorithms,San Mateo, California, 1993, 416-423.[3] Deb, K., Agrawal, S., Pratap, A. Meyarivan, T. A Fast Elitist Non-dominated Sorting Genetic Algorithm forMultiobjective Optimization: NSGA-II. In: M. Schoenauer (eds) Parallel Problem Solving from Nature, 2000,849-858.[4] Corne, D.W., Knowles, J.D., Oates, H.J. The Pareto-Envelope based Selection Algorithm for MultiobjectiveOptimisation. In: M. Schoenauer (eds) Parallel Problem Solving from Nature,Lecture Notes in ComputerScience, 1917, 2000, 869-878.[5] Zitler, E., Thiele, L. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the StrengthPareto Approach. IEEE Transactions on Evolutionary Computation, Vol.3 No.4, 1999, 257-271.[6] Zitzler, E., Laumanns, M., Thiele, L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TechnicalReport 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology(ETH) Zurich, Switzerland, 2001.[7] Deb, K., Goel, T. Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence.EMO In: E. Zitzler et al. (eds): Lecture Notes in Computer Science, 2001, 67-81.[8] Laumanns, M., Zitzler, E., Thiele, L. On the Effects of Archiving, Elitism, and Density Based Selection inEvolutionary Multi-objective Optimization. EMO In: E. Zitzler et al. (eds):Lecture Notes in Computer Science,2001, 181-196[9] Knowles, J. D., Corne, D. W. The Pareto Archived Evolution Strategy: A New Baseline Algorithm for ParetoMultiobjective Optimisation. In Congress on Evolutionary Computation, Vol. 1, Piscataway, NJ. IEEE Press,1999, 98-105.[10] Deb, K. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, 2002.[11] Coello, C. A., Van Veldhuizen, D. A., Lamont, G.B. Evolutionary Algorithms for Solving Multi-ObjectiveProblems. Kluwer Academic Publishers, 2002.[12] Macready, W.G.; Wolpert, D.H. The No Free Lunch theorem. IEEE Trans. on Evolutionary Computing, Vol. 1,No. 1, 1997, 67-82.