Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

doi:10.1109/TEVC.2002.804320

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Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

2002-12-01

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Kuk-Hyun Han

Jong-Hwan Kim

DOI：10.1109/TEVC.2002.804320

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Abstract

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This paper proposes it novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and super-position of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.

Keywords:

evolutionary algorithm

knapsack problem

probabilistic representation

quantum computing