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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15804
Title: Toward a more Generalized Quantum-Inspired Evolutionary Algorithm for Combinatorial Optimization Problems
Authors: Alegría Reymer, Julio Manuel
Túpac Valdivia, Yván Jesús
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
Issue Date: 2017
Publisher: IEEE Computer Society
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011891484&doi=10.1109%2fSCCC.2013.30&partnerID=40&md5=7d43b7bddcd73c084de3fc5c64b1e468
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.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15804
ISBN: 9781509004263
ISSN: 15224902
Appears in Collections:Artículos de investigación

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