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Improvement of evolutionary algorithm based on schema exploiter

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Języki publikacji
EN
Abstrakty
EN
Stochastic Schemata Exploiter (SSE) is one of the evolutionary optimization algorithms for solving the combinatorial optimization problems. We present the Extended SSE (ESSE) algorithm which is composed of the original SSE and new ESSE operations. The ESSE is compared with the original SSE, simple genetic algorithm (SGA), and GA with Minimal Generation Gap (MGG) in some test problems in order to discuss its features.
Rocznik
Strony
85--98
Opis fizyczny
Bibliogr. 10 poz., rys. tab., wykr.
Twórcy
autor
autor
  • Graduated School of Information Sciences, Nagoya University, Nagoya 464-8601, Japan
Bibliografia
  • [1] D. Yoshizawa, H. Sakanoh, S. Hashimoto. Coarse-graining Newton's method for optimization (in Japanese). Trans. IPSJ, Mathematical Modeling and Problem Solving, 20: 21-24, 2003.
  • [2] J.H. Holland. Adaptation in Natural and Artificial Systems, 1 ed. The University of Michigan Press, 1975.
  • [3] D.E. Goldberg. Genetic Algorithms m Search, Optimization and Machine Learning, l ed. Addison Wesley, 1989.
  • [4] D. Whitly. The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials in best. In J.D. Shafer, ed., Proc. 3rd Int. Conf. Genetic Algorithm, pp. 116-121. Morgan Kaufmann Pub., 1989.
  • [5] J.E. Baker. Reducing bias and inefficiency in the selection algorithm. Proceedings of the Second International Conference on Genetic Algorithms, pp. 14-21, 1987.
  • [6] H. Satoh, I. Ono, S. Kobayashi. Minimal generation gap model for gas considering both exploration and exploitation. Proc. IIZUKA '96, pp. 494-497, 1996.
  • [7] I. Ono, S. Kobayashi, K. Yoshida. Global and multi-objective optimization for lens design by real-coded genetic algorithms. International Optical Design Conference, 3482: 110-121, 1998.
  • [8] N.A. Aizawa. Evolving SSE: A stochastic schemata exploiter. In Proc. 1st IEE Conf. Evol. Comp., pp. 525-529. IEEE, 1994.
  • [9] L.D. Whitley. Fundamental principles of deception in genetic search. In G.J.E. Rawlins, ed., Foundations of Genetic Algorithms 1991 (FOGA 1), pp. 221-241. Morgan Kaufmann, 1991.
  • [10] D.S. Johnson, C.R. Aragon, L.A. McGeoch, C. Schevon. Optimization by simulated annealing: An experimental evaluation. Part I, Graph partitioning. Operations Research, 37: 865-892, 1989.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB8-0009-0001
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