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Fonseca, C.M.1
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2
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84947926042
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A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II
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Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the Parallel Problem Solving from Nature VI (PPSN-VI), pp. 849-858 (2000)
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Deb, K.1
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3
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0004140075
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SPEA2: Improving the strength pareto evolutionary algorithms
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Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH) Zurich, May
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Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithms. TIK report 103, Computer Engineering and Networks Laboratory (TIK), Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH) Zurich, May (2001)
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4344625151
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A fast multi-objective evolutionary algorithms for finding well-spread pareto-optimal solutions
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Indian Institute of Technology, Kanpur, India
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Deb, K., Mohan, M., Mishra, S.: A fast multi-objective evolutionary algorithms for finding well-spread pareto-optimal solutions, KanGAL Report 2003002, Indian Institute of Technology, Kanpur, India (2003)
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KanGAL Report 2003002
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Deb, K.1
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6
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HypE: An algorithm for fast hypervolume-based many-objective optimization
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Computer Engineering and Networks Laboratory (TIK), Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland, November
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Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume-based many-objective optimization. TIK report 286, Computer Engineering and Networks Laboratory (TIK), Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland, November (2008)
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TIK Report 286
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Bader, J.1
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SMS-EMOA: Multi-objective selection based on dominated hypervolume
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Beume N., Naujoks B., Emmerich M.: SMS-EMOA: multi-objective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653-1669 (2007)
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Wagner T., Beume N., Naujoks B.: Pareto- Aggregation-, and Indicator-based Methods in Many-Objective Optimization, Evolutionary Multi-Criterion Optimization. Springer, Berlin (2007)
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Approximating the nondominated front using the pareto archive evolutionary strategy
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Knowles J.D., Corne D.W.: Approximating the nondominated front using the pareto archive evolutionary strategy. Evol. Comput. 8(2), 149-172 (2000)
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Reducing bias and inefficiency in the selection algorithm
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Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Grefenstette, J.J. (ed.) Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, pp. 14-21 (1987)
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Simulated binary crossover for continuous search space
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Deb K., Agrawal R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115-148 (1995)
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A combined genetic adaptive search (GeneAS) for engineering design
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Deb K., Goyal M.: A combined genetic adaptive search (GeneAS) for engineering design. Comput. Sci. Inform. 26(4), 30-45 (1996)
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PISA - A platform and programming language independent interface for search algorithms
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Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) Springer, Berlin
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Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA - a platform and programming language independent interface for search algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) Evolutionary Multi-Criterion Optimization, pp. 494-508. Springer, Berlin (2003)
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Coello C.A.C., Pulido G.T., Montes E.M.: Current and Future Research Trends in Evolutionary Multi-objective Optimization, Information Processing with Evolutionary Algorithms: from Industrial Applications to Academic Speculations, pp. 213-231. Springer, London (2005)
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Multi-objective evolutionary computation for supersonic wing-shape optimization
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Obayashi S., Sasaki D., Takeguchi Y., Hirose N.: Multi-objective evolutionary computation for supersonic wing-shape optimization. IEEE Trans. Evol. Comput. 4(2), 182-187 (2000)
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Design optimization of exhaust manifold for a car engine using MOGA
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Athens, September. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailiou K., Fogarty T. (eds.) Evolutionary Methods for Design, Optimisation and Control, c CIMNE, Barcelona
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Kanazaki, M., Obayashi, S., Nakahashi, K.: Design optimization of exhaust manifold for a car engine using MOGA. Presented at EUROGEN2001, Athens, September. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailiou K., Fogarty T. (eds.) Evolutionary Methods for Design, Optimisation and Control, c CIMNE, Barcelona (2002)
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Comparison of multi-objective evolutionary algorithms: Empirical results
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Zitzler E., Deb K., Thiele L.: Comparison of multi-objective evolutionary algorithms: empirical results. Evol. Comput. J. 8(2), 125-148 (2000)
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Scalable Test Problems for Evolutionary Multi-Objective Optimization
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A. Abraham R. Jain R. Goldberg (eds) Chapter 6 Springer Berlin
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Deb K., Thiele L., Laumanns M., Zitzler E.: Scalable Test Problems for Evolutionary Multi-Objective Optimization. In: Abraham, A., Jain, R., Goldberg, R. (eds.) Evolutionary Multi-objective Optimization: Theoretical Advances and Applications, Chapter 6, pp. 105-145. Springer, Berlin (2005)
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A tutorial on the performance assessment of stochastic multi-objective optimizers
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Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Switzerland, February
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Knowles, J., Thiele, L. Zitzler, E.: A tutorial on the performance assessment of stochastic multi-objective optimizers. TIK report 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Switzerland, February (2006)
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Performance assessment of multi-objective optimizers: An analysis and review
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Zitzler E., Thiele L., Laumanns M., Fonseca C.M., da Fonseca V.G.: Performance assessment of multi-objective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7, 117-132 (2003)
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