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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15827
Title: FP-AK-QIEA-R for Multi-Objective optimization
Authors: Saire, Josimar
Keywords: Bioinformatics;Interpolation;Iterative methods;Multiobjective optimization;Optimization;Probability density function;Benchmark functions;Cumulative density functions;Evolutionary operations;Initial population;Multi-objective problem;Particle filter;PDF estimation;Quantum inspired evolutionary algorithm;Evolutionary algorithms
Issue Date: 2016
Publisher: Association for Computing Machinery
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014862631&doi=10.1145%2f3022702.3022714&partnerID=40&md5=46c1af0f44069678768297d9e398f2bd
Abstract: The Evolutionary Algorithms have main features like: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The FP-AK-QIEA-R uses probability density function according to best of initial population to sample new population and uses rewarding criteria to sample around the best of every iteration using cumulative density function estimated for Akima interpolation, it was used for mono-objective problems showing good results. The proposal uses the algorithm FP-AKQIEA-R and add Pareto dominance to experiment with multiobjective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm. © 2016 ACM.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15827
ISBN: urn:isbn:9781450348249
Appears in Collections:Artículos - Ciencia de la computación

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