Computer Science – Neural and Evolutionary Computing
Scientific paper
2010-04-04
In Proceedings of the 12th annual conference companion on Genetic and evolutionary computation (GECCO '10). ACM, New York, NY,
Computer Science
Neural and Evolutionary Computing
2 pages, 2 figures, 1 table, late-breaking
Scientific paper
10.1145/1830761.1830878
This paper extends the analogies employed in the development of quantum-inspired evolutionary algorithms by proposing quantum-inspired Hadamard walks, called QHW. A novel quantum-inspired evolutionary algorithm, called HQEA, for solving combinatorial optimization problems, is also proposed. The novelty of HQEA lies in it's incorporation of QHW Remote Search and QHW Local Search - the quantum equivalents of classical mutation and local search, that this paper defines. The intuitive reasoning behind this approach, and the exploration-exploitation balance thus occurring is explained. From the results of the experiments carried out on the 0,1-knapsack problem, HQEA performs significantly better than a conventional genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms - QEA and NQEA, in terms of convergence speed and accuracy.
Hota Ashish Ranjan
Koppaka Sisir
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