Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

Abstract

This paper proposes a 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 superposition 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.Index Terms-Evolutionary algorithm, knapsack problem, probabilistic representation, quantum computing.

Quantum Genetic Algorithms for Computer Scientists

“…Since the origins of quantum genetic algorithms (QGAs) [9] until today, lots of QGAs have been proposed in the scientific literature [37][38][39][40][41]. All kinds of quantum evolutionary algorithms have been successfully applied to optimization problems, such as the personnel scheduling problem [42], dynamic economic dispatch problem, e.g., in power systems [43], multi-sensor image registration [44], cryptanalysis [45], etc.…”

“…In any case, and without going into theoretical issues, since 1956 [8] when was first proposed the genetic algorithms Evolutionary Computation has made great progress. In 2002 Han [9] introduced a novel evolutionary algorithm inspired by quantum computing, growing from this date the number of publications on quantum-inspired genetic algorithms. In 2010 Ying [10] proposed that quantum computing could be used to achieve certain goals in Artificial Intelligence (AI).…”

An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy

“…Quantum evolutionary algorithm (QEA) is proposed by combining quantum theory with evolutionary theory. It is a kind of evolutionary algorithm with the form of quantum chromosome; the random observation which simulates the quantum collapse can bring diverse individuals [1]. In QEA, the quantum bits (Q-bits) and quantum gate (Q-gate) have been used to update the individuals to realize evolutionary search.…”

Improved Quantum-Inspired Evolutionary Algorithm and Its Application to 3-SAT Problems

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