Title: A quantum genetic algorithm based on cellular automata model

Authors: Xuewen Xia; Qian Wang; Yuanxiang Li

Addresses: Department of Computer and Information Science, Hubei Engineering University, Hubei, 432000, China ' School of Mathematical Science, Xinjiang Normal University, Xinjiang, 830053, China ' Department of Computer Science, Wuhan University, Hubei, 430079, China

Abstract: A novel quantum genetic algorithm based on cellular automata (CA) is proposed called cellular automata model quantum genetic algorithm (CA-QGA). In CA-QGA, each individual in population is mapped to a cell in CA, and the evolutionary of population is realised by the cells' iteration based on a function. Under the guidance of the function, some helpful information in each excellent individual can spread out across the whole population through much iteration. Aimed to maintain the diversity of population and help algorithm jump out of a local optimal solution, a quantum catastrophe is adopted when the best individual no longer improves. It is proved that this algorithm has lower time complexity and can converge to a global optimal solution with probability 1. Experimental results illustrate that the proposed algorithm has better performance.

Keywords: quantum genetic algorithms; QGA; cellular automata; function optimisation.

DOI: 10.1504/IJMIC.2013.052818

International Journal of Modelling, Identification and Control, 2013 Vol.18 No.3, pp.243 - 250

Published online: 16 Aug 2014 *

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