An approach to real-coded quantum inspired evolutionary algorithm using particles filter
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Date
2016
Journal Title
Journal ISSN
Volume Title
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.
Description
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