Toward a more Generalized Quantum-Inspired Evolutionary Algorithm for Combinatorial Optimization Problems
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Date
2017
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Journal ISSN
Volume Title
Publisher
IEEE Computer Society
Abstract
In this paper, a generalization of the original Quantum-Inspired Evolutionary Algorithm (QIEA): the Generalized Quantum-Inspired Evolutionary Algorithm (GQIEA) is proposed. Like QIEA, GQIEA is also based on the quantum computing principle of superposition of states, but extending it not only to be used for binary values {0, 1}, but for any finite set of values {1,?, n}. GQIEA, as any other quantum inspired evolutionary algorithm, defines an own quantum individual, an evaluation function and population operators. As in QIEA, GQIEA also defines a generalized Q-gate operator, which is a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, the proposal will be applied to the Knapsack Problem (KP), a classic combinatorial optimization problem. Results show that GQIEA has a good performance even with a small population. © 2015 IEEE.
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Keywords
Combinatorial optimization , Optimization , Quantum computers , Quantum theory , Combinatorial optimization problems , Evaluation function , Knap-sack problem , Knapsack problems , Principle of superposition , Quantum Computing , Quantum inspired evolutionary algorithm , Variation operator , Evolutionary algorithms