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.Comment: 2 pages, 2 figures, 1 table, late-breakin