Please use this identifier to cite or link to this item:
|Title:||FP-AK-QIEA-R for Multi-Objective optimization|
|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|
|Publisher:||Association for Computing Machinery|
|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.|
|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.