An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization
Neural and Evolutionary Computing
2014-09-29 v1
Abstract
This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction of the size of the Pareto optimal set.
Cite
@article{arxiv.1409.7478,
title = {An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization},
author = {Hernan Aguirre and Arnaud Liefooghe and Sébastien Verel and Kiyoshi Tanaka},
journal= {arXiv preprint arXiv:1409.7478},
year = {2014}
}
Comments
apperas in Parallel Problem Solving from Nature - PPSN XIII, Ljubljana : Slovenia (2014)