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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15796
Title: Parameters analysis of QIEA-R in convergence quality
Authors: Chire Saire, Josimar Edinson
Túpac Valdivia, Yván Jesús
Keywords: Benchmarking;Population statistics;Quality control;Quantum theory;Benchmark functions;Convergence analysis;Number of iterations;Numerical problems;Parameters analysis;Quantum inspired evolutionary algorithm;Quantum population;Quantum superpositions;Evolutionary algorithms
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015207479&doi=10.1109%2fANDESCON.2016.7836209&partnerID=40&md5=33c517c1030918e8d8689c86f1ee2231
Abstract: QIEA-R (Quantum Inspired Evolutionary Algorithm with Real Codification) was proposed for solving numerical problems obtaining better results when compared with traditional EAs, DE and PSO algorithms. It is inspired on the concept of quantum superposition in order to reduce the number of evaluations. QIEA-R has two important steps: initialization of the quantum population and updating of the quantum population. This paper analyzes these two steps and parameters related: Size of classical population, number of iterations, over some benchmark functions using statistical measurements to evaluate their importance and effect in convergence quality. The results shows the importance of quantum population size and update frequency. © 2016 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15796
ISBN: 9781509025312
Appears in Collections:Artículos de investigación

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