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Improvement of Quantum Evolutionary Algorithm with Functional Sized Population

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  • Computer Science
  • Mathematics

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Title J. Mehnen et al. (Eds.): Applications of Soft Computing, AISC 58, pp. 389 – 398. springerlink.com © Springer-Verlag Berlin Heidelberg 2009 Improvement of Quantum Evolutionary Algorithm with a Functional Sized Population Tayarani Mohammad and Akbarzadeh Toutounchi Mohammad Reza∗∗ Abstract. This paper proposes a dynamic structured interaction among members of population in a Quantum Evolutionary Algorithms (QEA). The structured population is allowed to expand/collapse based on a functional population size and partial re- initialization of new members in the population. Several structures are compared here and the study shows that the best structure for QEA is the cellular structure which can be an efficient architecture for an effective Exploration/Exploitation tradeoff, and the partial re-initialization of the proposed algorithm can improve the diversity of the algorithm. The proposed approach is tested on Knapsack Problem, Trap Problem as well as 14 numerical optimization functions. Experimental results show that the proposed Structure consistently improves the performance of QEA. 1 Introduction Recently we proposed a ring structure sinusoid sized population for QEA (SRQEA) [1]. In this paper several structures and functions for the population of QEA are investigated to find the best population structure and function for the size of the population.. Size of the population is an effective parameter of the evolutionary algorithms and has a great role on the performance of EAs. Several researches investigate the effect of population size and try to improve the performance of EAs by controlling of the size of the population. A functional sized population GA with a periodic function of saw-tooth function is proposed in [2]. Reference [3] finds the best population size for genetic algorithms. Inspired by the natural features of the variable size of the population, [4] presents an improved genetic algorithm with variab

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