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Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons

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Abstract

Evolutionary techniques for multi-objective(MO) optimization are currently gainingsignificant attention from researchers invarious fields due to their effectiveness androbustness in searching for a set of trade-offsolutions. Unlike conventional methods thataggregate multiple attributes to form acomposite scalar objective function,evolutionary algorithms with modifiedreproduction schemes for MO optimization arecapable of treating each objective componentseparately and lead the search in discoveringthe global Pareto-optimal front. The rapidadvances of multi-objective evolutionaryalgorithms, however, poses the difficulty ofkeeping track of the developments in this fieldas well as selecting an existing approach thatbest suits the optimization problem in-hand.This paper thus provides a survey on variousevolutionary methods for MO optimization. Manywell-known multi-objective evolutionaryalgorithms have been experimented with andcompared extensively on four benchmark problemswith different MO optimization difficulties.Besides considering the usual performancemeasures in MO optimization, e.g., the spreadacross the Pareto-optimal front and the abilityto attain the global trade-offs, the paper alsopresents a few metrics to examinethe strength and weakness of each evolutionaryapproach both quantitatively and qualitatively.Simulation results for the comparisons areanalyzed, summarized and commented.

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References

  • Adeli, H. & Cheng, N. T. (1994). Augmented Lagrangian genetic algorithm for structural optimization. Journal of Aerospace Engineering 7: 104–118.

    Google Scholar 

  • Aizawa, A. N. & Wah, B. W. (1993). Dynamic control of genetic algorithms in a noisy environment. In Forrest, S. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, 48–55. M. Kaufmann Publishers, San Mateo.

    Google Scholar 

  • Allenson, R. (1992). Genetic algorithms with gender for multi-function optimization. Technical Report: EPCC-SS92-01, Edinburgh Parallel Computing Center, Edinburgh, Scotland.

    Google Scholar 

  • Anderson, J. M., Sayers, T. M. & Bell, M. G. H. (1998). Optimisation of a fuzzy logic traffic signal controller by a multiobjective genetic algorithm. IEE Road Transport Information and Control 454: 186–190.

    Google Scholar 

  • Andrzej, O. & Stanislaw, K. (2000). A new constraint tournament selection method for multicriteria optimization using genetic algorithm. IEEE International Conference on Evolutionary Computation: 501-507.

  • Angeline, P. J. (1996). The effect of noise on self-adaptive evolutionary optimization. In Fogel, L. J., Angeline, P. J. & Back, T. (eds.) Proceedings of the Fifth Annual Conference on Evolutionary Programming, 433–439. MIT Press, Cambridge, Mass.

    Google Scholar 

  • Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York.

    Google Scholar 

  • Beale, E. M. (1988). Introduction to Optimization. Wiley-Interscience Series in Discrete Mathematics and Optimization, John Wiley & Sons.

  • Beasley, D., Bull, D. R. & Martin, R. R. (1993). A sequential niche technique for multimodal function optimization. Evolutionary Computation 1(2): 101–125. MIT Press Journals.

    Google Scholar 

  • Ben-Tal, A. (1980). Characterization of Pareto and lexicographic optimal solution. In Fandel, G. & Gal, T. (eds.) Multiple Criteria Decision Making Theory and Application 177, 1–11 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin.

    Google Scholar 

  • Bentley, P. J. & Wakefield, J. P. (1997). Finding acceptable solutions in the Pareto-optimal range using multiobjective genetic algorithms. In Proceedings of the 2nd On-Line World Conference on Soft Computing in Engineering Design and Manufacturing (WSC2), (http://users.aol.com/docbentley/dispaper.htm).

  • Branke, J. (1999). Memory enhanced evolutionary algorithms for changing optimization problems. IEEE International Conference on Evolutionary Computation 3: 1875–1882.

    Google Scholar 

  • Brans, J. P., Vincke, P. & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European journal of Operational Research 24(2): 228–238.

    Google Scholar 

  • Chambers, J. M., Cleveland, W. S., Kleiner, B. & Turkey, P. A. (1983). Graphical Methods for Data Analysis. Wadsworth & Brooks/Cole, Pacific CA.

    Google Scholar 

  • Charnes, A. & Cooper, W. W. (1961). Management Models and Industrial applications of Linear Programming 1. John Wiley, New York.

    Google Scholar 

  • Chen, S, J., Hwang, C. L. & Hwang, F. P. (1992). Fuzzy Multiple Attribute Decision Making, 265. Springer-Verlag.

  • Coello Coello, C. A. (1996). An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design. Ph.D. Thesis, Department of Computer Science, Tulane University, New Orleans, LA., (http://www.lania.mx/~ccoello/EMOO/EMOObib.html).

    Google Scholar 

  • Coello Coello, C. A. (1999). A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowledge and Information Systems 1(3): 269–308.

    Google Scholar 

  • Collard, P. & Escazut, C. (1995). Genetic operators in a dual genetic algorithm. International Conference on Tools and Artificial Intelligence, 12-19.

  • Cvetkoviż, D. & Parmee, I. C. (1999). Genetic algorithm-based multi-objective optimization and conceptual engineering design. IEEE International Conference on Evolutionary Computation 1: 29–36.

    Google Scholar 

  • Dasgupta, D. & McGregor, D. R. (1992). Nonstationary function optimization using the structured genetic algorithm. In Männer, R. & Manderick, B. (eds.) Parallel Problem Solving from Nature 2, 145–1154. Amsterdam, North Holland.

    Google Scholar 

  • Davidor, Y. (1991). Epistasis variance: A viewpoint on GA-hardness. In Rawlins, G. J. E. (ed.), 23-35. Morgan Kaufmann.

  • De Jong, K. A. (1975). Analysis of the behavior of a class of genetic adaptive systems. Ph. D Dissertation, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, MI.

    Google Scholar 

  • Deb, K. (1995). Optimization for Engineering Design: Algorithms and Examples. Prentice Hall, New Delhi.

    Google Scholar 

  • Deb, K. (1999a). Evolutionary algorithms for multi-criterion optimization in engineering design. In Miettinen, K. et al. (ed.) Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming, and Industrial Applications. Wiley, New York.

    Google Scholar 

  • Deb, K. (1999b). Multi-objective genetic algorithms: Problem difficulties and construction of test problem. Journal of Evolutionary Computation 7(3): 205–230. The MIT Press.

    Google Scholar 

  • Deb, K. (1999c). Construction of test problems for multi-objective optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99) 1: 164–171.

    Google Scholar 

  • Deb, K. (2001). Multi-objective Optimization using Evolutionary Algorithms. John Wiley & Sons, London.

    Google Scholar 

  • Deb, K. & Goldberg, D. E. (1989). An investigation on niche and species formation in genetic function optimization. In Schaffer, J. D. (ed.) Proc. of Third Int. Conf. on Genetic Algorithms, 42–50. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Eshenauer, H., Koski, J. & Osyczka, A. (eds.) (1990). Multicriteria Design Optimization. Berlin, Germany: Springer-Verlag.

    Google Scholar 

  • Fitzpatrick, J. M. & Grefenstette, J. J. (1988). Genetic algorithms in noisy environment. Machine Learning 3(2/3): 101–120.

    Google Scholar 

  • Fonseca, C. M. & Fleming, P. J. (1993). Genetic algorithm for multiobjective optimization, formulation, discussion and generalization. In Forrest, S. (ed.) Genetic Algorithms: Proceeding of the Fifth International Conference, 416–423. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Fonseca, C. M. & Fleming, P. J. (1995a). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1): 1–16.

    Google Scholar 

  • Fonseca, C. M. & Fleming, P. J. (1995b). Multi-objective genetic algorithm made easy: Selection, sharing and mating restriction. International Conference on Genetic Algorithm in Engineering Systems: Innovations and Application, 12-14. UK.

  • Fonseca, C. M. & Fleming, P. J. (1997). Multiobjective optimization. In Bäck, T., Fogel, D. & Michalewicz, Z. (eds.) Handbook of Evolutionary Computation 1: C4.5:1–C4.5:9. Oxford University Press, England.

    Google Scholar 

  • Fonseca, C. M. & Fleming, P. J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part I: A unified formulation," IEEE Trans. On System, Man, and Cybernetics-Part A: System and Humans 28(1): 26–37.

    Google Scholar 

  • Forrest, S., Javornik, B., Smith, R. E. & Perelson, A. S. (1993). Using genetic algorithms to explore pattern recognition in the immune system. Evolutionary Computation 1(3): 191–211. MIT Press Journals.

    Google Scholar 

  • Fourman, M. P. (1985). Compaction of symbolic layout using genetic algorithms. In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, 141-153. Lawrence Erlbaum.

  • Fujita, K., Hirokawa, N., Akagi, S., Kitamura, S. & Yokohata, H. (1998). Multi-objective design automotive engine using genetic algorithm. Proc. of 1998 ASME Design Engineering Technical Conferences, 1-11. Atlanta, Georgia.

  • Gaspar, A. & Collard, P. (1997). Time dependent optimization with a folding genetic algorithm. IEEE International Conference on Tools with Artificial Intelligence, 125-132.

  • Ghosh, A., Tsutsui, S. & Tanaka, H. (1996). Individual aging in genetic algorithms. Conference on Intelligent Information Systems, 276-279.

  • Goldberg, D. E. (1987). Simple genetic algorithms and the minimal, deceptive problem. In Genetic Algorithms and Simulated Annealing, 74-88. Morgan Kaufmann.

  • Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading, Massachusetts.

    Google Scholar 

  • Goldberg, D. E. & Richardson, J. (1987). Genetic algorithms with sharing for multi-modal function optimization. Proc. 2nd Int. Conf. on Genetic Algorithms, 41-49. Lawrence Erlbaum.

  • Greenwood, G. W., Hu, X. & D'Ambrosio, J. G. (1996). Fitness functions for multiple objective optimization problems: Combining preferences with Pareto rankings. In Foundations of Genetic Algorithms 4 (FOGA-96), 437-455. Morgan Kaufmann.

  • Grefenstette, J. J. (1992). Genetic algorithms for changing environments. In Männer, R. & Manderick, B. (eds.) Parallel Problem Solving from Nature 2, 137–144. Amsterdam, North Holland.

    Google Scholar 

  • Haas, O. C., Burnham, K. J. & Mills, J. A. (1997). On improving physical selectivity in the treatment of cancer: A systems modelling and optimization approach. Control Engineering Practice 5(12): 1739–1745.

    Google Scholar 

  • Hajela, P. & Lin, C. Y. (1992). Genetic search strategies in multicriterion optimal design. Journal of Structural Optimization 4: 99–107.

    Google Scholar 

  • Hiroyasu, T., Miki, M. & Watanabe, S. (1999). Distributed genetic algorithm with a new sharing approach in multiobjective optimization problems. IEEE International Conference on Evolutionary Computation 1: 69–76.

    Google Scholar 

  • Horn, J. (1997). Multicriterion decision making. In Bäck, T., Fogel, D. & Michalewicz, Z. (eds.) Handbook of Evolutionary Computation 1: F1.9:1–F1.9:15. Oxford University Press, Oxford, England.

    Google Scholar 

  • Horn, J. & Goldberg, D. E. (1995). Genetic algorithm difficulty and the modality of fitness landscape. In Whitley, L. D. & Vose, M. D. (eds.) Proceedings of the Third Workshop on Foundation of Genetic Algorithms, 243-270. Morgan Kaufmann.

  • Horn, J. & Nafpliotis, N. (1993). Multiobjective Optimization Using the Niched Paareto Genetic Algorithm. Technical Report 930005, University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 South Matthews Avenue, Urbana, IL 61801-2996: Illinois Genetic Algorithms Laboratory (IlliGAL).

    Google Scholar 

  • Horn, J., Nafpliotis, N. & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimisation. IEEE international Conference on Evolutionary Computation 1: 82–87.

    Google Scholar 

  • Ijiris, Y. (1965). Management Goals and Accounting for Control. Amsterdam, North-Holland.

    Google Scholar 

  • Jakob, W., Gorges-Schleuter, M. & Blume, C. (1992). Application of genetic algorithms to task planning and learning. In (Männer, R. & Nanderick, B. (eds.) Parallel Problem Solving from Nature, 2nd Workshop, 291-300. Lecture Notes in Computer Science (Amsterdam), North-Holland Publishing Company.

  • Jaszkiewicz, A. (1998). Genetic local search for multiple objective combinatorial optimization. Technical Report RA-014/98, Institute of Computing Science, Poznan University of Technology.

  • Jutler, H. (1967). Liniejnaja modiel z nieskolkimi celevymi funkcjami (linear model with several objective functions). Ekonomika i matematiceckije Metody 3, 397–406.

    Google Scholar 

  • Kargupta, H. (1995). Signal-to-noise, crosstalk, and long range problem difficulty in genetic algorithms. In Eshelman, L. J. (ed.) Proceedings of the 6th International Conference on Genetic Algorithms, 193-200. Morgan Kaufmann.

  • Keeney, R. L. & Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Trade-offs. Wiley, New York.

    Google Scholar 

  • Kita, H., Yabumoto, Y., Mori, N. & Nishikawa, Y. (1996). Multi-objective optimization by means of the thermodynamical genetic algorithm. In Voigt, H-M. (eds.) The 4th International conference on Parallel Problem solving from Nature, 504–512. Springer, New York.

    Google Scholar 

  • Khor, E. F., Tan, K. C., Wang, M. L. & Lee, T. H. (2000). Evolutionary algorithm with dynamic population size formulti-objective optimization. Conf. on Simulated Evolution and Learning 2000, 2768-2773. (SEAL'2000), Nagoya, Japan.

  • Khor, E. F., Tan, K. C. & Lee, T. H. (2001). Tabu-based exploratory evolutionary algorithm for effective multi-objective optimization. First International Conference on Evolutionary Multi-Criterion Optimization (EMO'01), 344-358, Zurich, Switzerland.

  • Knowles, J. D. & Corne, D. W. (1999). The Pareto archived evolution strategy: A new baseline algorithm for multi-objective optimization. IEEE International Conference on Evolutionary Computation, 98-105.

  • Knowles, J. D. & Corne, D. W. (2000). M-PAES: A memetic algorithm for multiobjective optimization. IEEE International Conference on Evolutionary Computation, 325-333.

  • Laumanns, M., Rudolph, G. & Schwefel, H-P. (1998). A spatial predator-prey approach to multi-objective optimization: A preliminary study. In Eiben, A. E., Schoenauer, M. & Schewefel, H-P. (eds.) Parallel Problem Solving From Nature-PPSN V, 241–249. Springer-Verlag, Holland.

    Google Scholar 

  • Lin, D. S. & Leou, J. J. (1997). A genetic algorithm approach to Chinese handwriting normalization. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 27(6): 999–1007.

    Google Scholar 

  • Lis, J. & Eiben, A. E. (1997). A multi-sexual genetic algorithm for multiobjective optimization. IEEE International Conference on Evolutionary Computation, 59-64.

  • Mahfoud, S. W. (1995). Niching methods for genetic algorithms. Ph.D. dissertation, University of Illinois, Urbana-Champaign.

    Google Scholar 

  • Marcu, T. (1997). A multiobjective evolutionary approach to pattern recognition for robust diagnosis of process faults. International Conference on IFAC Fault Detection, Supervision and Safety for Technical Processes, 1183-1188.

  • Mariano, C. E. & Morales, E. F. (2000). Distributed reinforcement learning for multiple objective optimization problems. IEEE International Conference on Evolutionary Computation 1: 188–194.

    Google Scholar 

  • Merz, P. & Freisleben, B. (1998). On the effectiveness of evolutionary search in highdimensional NK-landscape. IEEE International Conference on Evolutionary Computation 1: 741–745.

    Google Scholar 

  • Miller, B. L. & Shaw, M. J. (1996). Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization. IEEE International Conference on Evolutionary Computation, 786-791. Nagoya, Japan.

  • Miller, B. L. & Goldberg, D. E. (1995). Genetic algorithms, selection schemes and the varying effects of noise. Department of General Engineering, University of Illinois at Urbana-Champaign, 117 Transportation Building, Urbana, IL 61801.

  • Morimoto, T., Torii, T. & Hashimoto, Y. (1995). Optimal control of physiological processes of plants in a green plant factory. Control Engineering Practice 3(4): 505–511.

    Google Scholar 

  • Murata, T. & Ishibuchi, H. (1995). MOGA: Multi-objective genetic algorithms. IEEE International Conference on Evolutionary computation 1: 289–294.

    Google Scholar 

  • Osyczka, A. (1985). Multicriteria optimization for engineering design. In Gero, J. S. (ed.) Design Optimization, 193-227. Academic Press.

  • Pétrowski, A. (1996). A clearing procedure as a niching method for genetic algorithms. IEEE International Conference on Evolutionary Computation, 798-803. Nagoya, Japan.

  • Reformat, M., Kuffel, E., Woodford, D. & Pedrycz, W. (1998). Application of genetic algorithms for control design in power systems. IEE Proceedings on Generation, Transmission and Distribution 145(4): 345–354.

    Google Scholar 

  • Rekiek, B., Lit, P. D., Pellichero, F., L'Eglise, T., Falkenauer, E. & delchambre, A. (2000). Dealing with user's preferences in hybrid assembly lines design. Proceedings of the MCPI'2000 Conference.

  • Reklaitis, G. V., Ravindran, A. & Ragsdell, K. M. (1983). Engineering Optimization Methods and Applications. Wiley, New York.

    Google Scholar 

  • Richardson, J. T., Palmer, M. R., Liepins, G. & Hilliard, M. (1989). Some guidelines for genetic algorithms with penalty functions. In Schaffer J. D. (ed.) Proc. of Third Int. Conf. on Genetic Algorithms, 191-197.

  • Ritzel, B. J., Eheart, J. W. & Ranjithan, S. (1994). Using genetic algorithms to solve a multi objective groundwater pollution containment problem. Water Resources Research 30: 1589–1603.

    Google Scholar 

  • Romero, C. E. M. & Manzanares, E. M. (1999). MOAQ and Ant-Q algorithm for multiple objective optimization problems. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M. & Smith, R. E. (eds.) Genetic and Evolutionary Computing Conference (GECCO 99) 1: 894–901. Morgan Kaufmann, San Francisco.

    Google Scholar 

  • Sait, S. M., Youssef, H. & Ali, H. (1999). Fuzzy simulated evolution algorithm for multi-objective optimization of VLSI placement. IEEE International Conference on Evolutionary Computation 1: 91–97.

    Google Scholar 

  • Sakawa, M., Kato, K. & Shibano, T. (1996). An interactive fuzzy satisfying method for multiobjective multidimensional 0-1 knapsack problems through genetic algorithms. IEEE International Conference on Evolutionary Computation, 243-246.

  • Sandgren, E. (1994). Multicriteria design optimization by goal programming. In Adeli, H. (ed.) Advances in Design Optimization, 225–265. Chapman & Hall, London.

    Google Scholar 

  • Schaffer, J. D. (1985). Multi-objective optimization with vector evaluated genetic algorithms. In Genetic Algorithms and Their Applications: Proceedings of the First International Conference on Genetic Algorithms, 93-100. Lawrence Erlbaun.

  • Schaffer, J. D., Caruana, R. A., Eshelman, L. J. & Das, R. (1989). A study of control parameters affecting online performance of genetic algorithms for function optimization. Proceedings of Third International Conference on Genetic Algorithms, 51-60.

  • Sefrioui, M. & Periaux, J. (2000). Nash genetic algorithms: Examples and applications. IEEE International Conference on Evolutionary Computation 1: 509–516.

    Google Scholar 

  • Shaw, K. J., Notcliffe, A. L., Thompson, M., Love, J., Fonseca, C. M. & Fleming, P. J. (1999). Assessing the performance of multiobjective genetic algorithms for optimization of batch process scheduling problem. IEEE International Conference on Evolutionary Computation 1: 37–45.

    Google Scholar 

  • Solich, R. (1969). Zadanie programowania liniowego z wieloma funkcjami celu (linear programming problem with several objective functions). Przeglad Statystyczny 16: 24–30. (In Polish).

    Google Scholar 

  • Srinivas, N. & Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3): 221–248. MIT Press Journals.

    Google Scholar 

  • Steuer, J. (1986). Multi Criteria Optimization: Theory, Computation, and Application. John Wiley, New York, New York.

    Google Scholar 

  • Tagami, T. & Kawabe, T. (1999). Genetic algorithm based on a Pareto Neighbor search for multiobjective optimization. Proceedings of the 1999 International Symposium of Nonlinear Theory and its Applications (NOLTA'99), 331-334.

  • Tan, K. C., Lee, T. H. & Khor, E. F. (1999). Evolutionary algorithms with goal and priority information for multi-objective optimization. IEEE International Conference on Evolutionary Computation 1: 106–113.

    Google Scholar 

  • Tan, K. C., Lee, T. H. & Khor, E. F. (2001a). Evolutionary algorithms for multi-objective optimization: performance assessments and comparison, IEEE Congress on Evolutionary Computation, 979-986. Seoul, Korea.

  • Tan, K. C., Khor, E. F. & Lee, T. H. (2001b). Evolutionary algorithm for multi-objective optimization: A unified goal and priority approach. Journal of Artificial Intelligence Research, in press.

  • Tan, K. C., Lee, T. H. & Khor, E. F. (2001c). Evolutionary algorithm with dynamic population size and local exploration for multiobjective optimization. IEEE Transactions on Evolutionary Computation 5(6): 565–588.

    Google Scholar 

  • Tan, K. C., Lee, T. H., Khoo, D. & Khor, E. F. (2001d). A multi-objective evolutionary algorithm toolbox for computer-aided multi-objective optimization. IEEE Transactions on Systems, Man and Cybernetics: Part B (Cybernetics) 31(4): 537–556.

    Google Scholar 

  • The MathWorks, Inc. (1998). Using MATLAB. The Math Works Inc., Version 5.

  • Valenzuela-Redón, M. & Uresti-Charre, E. (1997). A non-generational genetic algorithm for multiobjective optimization. In Proceedings of the Seventh International Conference on Genetic Algorithms, 658–665. Morgan Kauffmann, San Francisco, California.

    Google Scholar 

  • Van Veldhuizen, D. A. & Lamont, G. B. (1998). Evolutionary computation and convergence to a Pareto front. In Koza, J. R. (ed.) Late Breaking Paper at the Genetic Programming 1998 Conference, 221–228. Stanford University Bookstore, Stanford University, California.

    Google Scholar 

  • Van Veldhuizen, D. A. & Lamont G. B. (1999). Multiobjective evolutionary algorithm test suites. Symposium on Applied Computing, 351-357. San Antonio, Texas.

  • Van Veldhuizen, D. A. & Lamont G. B. (2000). Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Journal of Evolutionary Computation 8(2): 125–147. The MIT Press.

    Google Scholar 

  • Vemuri, V. R. & Cedeino, W. (1995). A new genetic algorithm for multi-objective optimization in water resource management. IEEE International Conference on Evolutionary Computation 1: 495–500.

    Google Scholar 

  • Viennet, R., Fonteix, C. & Marc, I. (1996). Multicriteria optimization using a genetic algorithm for determining a Pareto set. International Journal of Systems Science 27(2): 255–260.

    Google Scholar 

  • Voget, S. & Kolonko, M. (1998). Multidimensional optimization with a fuzzy genetic algorithm. Journal of Heuristics 4(3): 221–244.

    Google Scholar 

  • Whitely, L. D. (1991). Fundamental principles of deception in genetic search. In Rawlins, G. (ed.) Foundations of Genetic Algorithms, 221–241. Morgan Kaufmann, San Mateo.

    Google Scholar 

  • Wilson, P. B. & Macleod, M. D. (1993). Low implementation cost IIR digital filter design using genetic algorithms. In IEE/IEEE Workshop on Natural Algorithms in Signal Processing, 4/1-4/8. Chelmsford, UK.

  • Zitzler, E. & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4): 257–271.

    Google Scholar 

  • Zitzler, E., Thiele, L. & Deb, K. (2000). Comparison of multiobjective evolutionary algorithms: Empirical Results. Journal of Evolutionary Computation 8(2): 173–195. The MIT Press.

    Google Scholar 

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Tan, K., Lee, T. & Khor, E. Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons. Artificial Intelligence Review 17, 251–290 (2002). https://doi.org/10.1023/A:1015516501242

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