Please use this identifier to cite or link to this item:
|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|
|Publisher:||Institute of Electrical and Electronics Engineers Inc.|
|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.|
|Appears in Collections:||Artículos de investigación|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.