FP-AK-QIEA-R for multi-objective optimization
Josimar Edinson Chire Saire · 2016
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.