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|Title:||An approach to real-coded quantum inspired evolutionary algorithm using particles filter|
|Authors:||Chire Saire, Josimar Edinson|
Túpac Valdivia, Yván Jesús
|Keywords:||Algorithms;Artificial intelligence;Bandpass filters;Distributed computer systems;Function evaluation;Monte Carlo methods;Optimization;Probability density function;Quantum theory;Signal filtering and prediction;Target tracking;Benchmark functions;Combined mechanisms;Multi-linear regression;Particle filter;PDF estimation;Quantum inspired evolutionary algorithm;Quantum superpositions;Relative frequencies;Evolutionary algorithms|
|Publisher:||Institute of Electrical and Electronics Engineers Inc.|
|Abstract:||This work proposes, implements and evaluates the FP-QIEA-R model as a new quantum inspired evolutionary algorithm based on the concept of quantum superposition that allows the optimization process to be carried on with a smaller number of evaluations. This model is based on a QIEA-R, but instead of just using quantum individuals based on uniform probability density functions, where the update consists on change the width and mean of each pdf; this proposal uses a combined mechanism inspired in particle filter and multilinear regression, re-sampling and relative frequency with the QIEA-R to estimate the probability density functions in a better way. To evaluate this proposal, some experiments under benchmark functions are presented. The obtained statistics from the outcomes show the improved performance of this proposal optimizing numerical problems. © 2015 IEEE.|
|Appears in Collections:||Artículos de investigación|
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