Superior Exploration-Exploitation Balance with Quantum-Inspired Hadamard Walks
Abstract
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.
Keywords
Cite
@article{arxiv.1004.0514,
title = {Superior Exploration-Exploitation Balance with Quantum-Inspired Hadamard Walks},
author = {Sisir Koppaka and Ashish Ranjan Hota},
journal= {arXiv preprint arXiv:1004.0514},
year = {2011}
}
Comments
2 pages, 2 figures, 1 table, late-breaking