FP-AK-QIEA-R for Multi-Objective Optimization

• Autor: Saire, JEC
• Formato: objeto de conferencia
• Fecha de Publicación: 2016
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/1075
• Enlace del recurso: https://hdl.handle.net/20.500.12390/1075; https://doi.org/10.1145/3022702.3022714
• Nivel de acceso: acceso abierto
• Materia: Herencia; Genética; Algoritmo; https://purl.org/pe-repo/ocde/ford#1.06.07

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-AK-QIEA-R and add Pareto dominance to experiment with multi-objective problems. The performed experiments use some benchmark functions from the literature and initial results shows a promissory way for the algorithm.