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Non-dominated Rank based Sorting Genetic Algorithms

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Języki publikacji
EN
Abstrakty
EN
In this paper a new concept of ranking among the solutions of the same front, along with elite preservation mechanism and ensuring diversity through the nearest neighbor method is proposed for multi-objective genetic algorithms. This algorithm is applied on a set of benchmark multi-objective test problems and the results are compared with that of NSGA-II (a similar algorithm). The proposed algorithm is seen to over perform the existing algorithm. More specifically, the new approach has been used to solve the deceptive multi-objective optimization problems in a better way.
Wydawca
Rocznik
Strony
231--252
Opis fizyczny
bibliogr. 33 poz., tab.
Twórcy
autor
autor
  • Machine Intelligence Unit, Indian Statistical Institute 203 B.T. Road, Calcutta 700 108, India, ash@isical.ac.in
Bibliografia
  • [1] Bentley, P. J. and Wakefield, J. P. (1997). Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms.
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  • [3] Coello, C. A. (2000). Handling preferences in evolutionary multiobjective optimization: a survey. In 2000 Congress on Evolutionary Computation, Vol. 1, pp. 30-37.
  • [4] Deb, K. Multi-Objective Optimization using Evolutionary Algorithms. Chichester, UK: Wiley, 2001
  • [5] Deb, K., Agarwal, S., Pratap, A. and Meyarivan, T. (2000b). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Proceedings of Parallel Problem Solving from Nature VI (PPSN-VI), pp. 849-858.
  • [6] DeJong, K. A. (1975). An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph. D. Thesis, Ann Arbor, MI: University of Michigan.
  • [7] Fonesca, C. M. and Fleming, P. J. (1993). Genetic algorithms for multi-objective optimization: formulation, discussion, and generalization. In Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416-423.
  • [8] Fonesca, C. M. and Fleming, P. J. (1995). An overview of evolutionary algorithms in multi-objective optimization. Evolutionary Computation Journal 3(1), pp. 1-16.
  • [9] Fonesca, C. M. and Fleming, P. J. (1996). On the performance assessment and comparison of stochastic multi-objective optimizers. In Proceedings of Parallel Problem Solving from Nature IV (PPSN-IV), pp. 584-593.
  • [10] Goldberg, D. E. (1989). Genetic Algorithms for Search, Optimization and Machine Learning. MA: Addison-Wesley.
  • [11] Goldberg, D. E. and Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 41-49.
  • [12] Hajela, P. and Lin, C. Y. (1992). Genetic search strategies in multi-criterion optimal design. Structural Optimization 4(2), pp. 99-107.
  • [13] Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: MIT Press
  • [14] Horn, J., Nafploitis, N. and Goldberg, D. (1994). A niched Pareto genetic algorithm for multi-objective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82-87.
  • [15] Knowles, J. D. and Corne, D. W. (2000). Approximating the non-dominated front using the Pareto archived evolution strategy. Evolutionary Computation Journal 8(2), pp. 149-172.
  • [16] Krishnakumar, K. (1998). Micro-genetic algorithms for stationery and non-stationery function optimization. In SPIE Proceedings: Intelligent Control and Adaptive Systems, pp. 289-296.
  • [17] Kursawe, F. (1990). A variant of evolutionary strategies for vector optimization. In Parallel Problem Solving from Nature I (PPSN-I), pp. 193-197.
  • [18] Laumanns, M., Rudolph, G. and Schwefel, H. P. (1998). A spatial predator-prey approach to multi-objective optimization: a preliminary study. In Proceedings of the Parallel Problem Solving from Nature V (PPSN-V), pp. 241-249.
  • [19] Leung, K. S., Zhu, Z. Y., Xu, Z. B. and Leung, Y. (1998). Multiobjective optimization using non-dominated sorting in annealing genetic algorithms. Department of Geography and Centre for Environmental Studies, Chinese University of Hong Kong, Hong Kong.
  • [20] Lirsawe, F. (1991). A variant of evolution strategies for vector optimization. In Parallel Problem Solving from Nature I (PPSN I), volume 496 of Lecture Notes in Computer Science, pp. 193-197.
  • [21] Murata, T. and Ishibuchi, H. (1995). MOGA: multi-objective genetic algorithms. In Proceedings of the Second IEEE International Conference on Evolutionary Computation, pp. 289-294.
  • [22] Neef, M., Thierens, D. and Arciszewski, H. (1999). A case study of a multiobjective recombinative genetic algorithm with coevolutionary sharing. In Proceedings of the Congress on Evolutionary Computation (CEC - 1999), pp. 796-803.
  • [23] Osyczka, A. and Kundu, S. (1995). A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. Structural Optimization 10(2), pp. 94-99.
  • [24] Rudolph, G. (2001). Evolutionary search under partially ordered fitness sets. In Proceedings of the International Symposium on Information Science Innovations in Engineering of Natural and Artificial Intelligent Systems (ISI 2001), pp. 818-822.
  • [25] Schaffer, J. D. (1984). Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms. Ph. D. Thesis, Nashville, TN: Vanderbit University.
  • [26] Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In proceedings of the first International Conference on Genetic Algorithms, pp. 93-100.
  • [27] Schott, J. R. (1995). Fault Tolerant Design Using Single and Multi-Criteria Genetic Algorithms. Master's Thesis, Boston, MA: Department of Aeronautics and Astronautics, Massachusetts Institute of Technology.
  • [28] Srinivas, N. and Deb, K. (1994). Multi-objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation Journal 2(3), pp. 221-248.
  • [29] Test problems for multiobjective optimizers (Eckart Zitzler's page), http://www.tik.ee.ethz.ch/ zit-zler/testdata.html.
  • [30] Veldhuizen, D. V. (1999). Multiobjective Evolutionary Algorithms: Classifiation, Analyses, and New Innovations. Ph. D. Thesis, Dayton, OH: Air Force Institute of Technology. Technical Report No. AFIT/DS/ENG/99-01.
  • [31] Zitzler, E. and Thiele, L. (1998a). An evolutionary algorithm for multiobjective optimization: The Strength Pareto approach. Technical Report 43, Zurich, Switzerland: Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology
  • [32] Zitzler, E. (1999). Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Ph. D. Thesis, Zurich, Switzerland: Swiss Federal Institute of Technology.
  • [33] Zitzler, E., Deb, K. and Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computational Journal 8(2), pp. 125-148
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0015-0049
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