Selective breeding in a multiobjective genetic algorithm
作者： G. T. Parks , I. Miller
DOI: 10.1007/BFB0056868
关键词:
摘要: This paper describes an investigation of the efficacy various elitist selection strategies in a multiobjective Genetic Algorithm implementation, with parents being selected both from current population and archive record nondominated solutions encountered during search. It is concluded that, because optimization process naturally maintains diversity population, it possible to improve performance algorithm through use strong elitism high pressures without suffering disadvantages genetic convergence which such would bring single objective optimization.
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sci-hub.se 参考文章(8)
GT Parks, PW Poon, Application of genetic algorithms to in-core nuclear fuel management optimization Kernforschungzentrum Karlsruhe. ,(1993)
James E. Baker, Adaptive Selection Methods for Genetic Algorithms international conference on genetic algorithms. pp. 101- 111 ,(1985)
N. Srinivas, Kalyanmoy Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms Evolutionary Computation. ,vol. 2, pp. 221- 248 ,(1994) , 10.1162/EVCO.1994.2.3.221
Carlos M. Fonseca, Peter J. Fleming, An overview of evolutionary algorithms in multiobjective optimization Evolutionary Computation. ,vol. 3, pp. 1- 16 ,(1995) , 10.1162/EVCO.1995.3.1.1
Thomas J. Downar, Alexander Sesonske, Light Water Reactor Fuel Cycle Optimization: Theory Versus Practice Advances in Nuclear Science and Technology. ,vol. 20, pp. 71- 126 ,(1988) , 10.1007/978-1-4613-9925-4_2
G. T. Parks, Multiobjective Pressurized Water Reactor Reload Core Design by Nondominated Genetic Algorithm Search Nuclear Science and Engineering. ,vol. 124, pp. 178- 187 ,(1996) , 10.13182/NSE96-A24233
PJ Turinsky, GT Parks, GI Maldonado, DJ Kropaczek, Application of simulated annealing to in-core nuclear fuel management optimization ,(1991)
David E. Goldberg, Genetic algorithms in search, optimization and machine learning Reading: Addison-Wesley. ,(1989)