Publications · 2009 · Conference paper
Ranking Methods for Many-Objective Problems
In MICAI 2009: Advances in Artificial Intelligence. 8th Mexican International Conference on Artificial Intelligence, 2009
Abstract
An important issue with Evolutionary Algorithms (EAs) is the way to identify the best solutions in order to guide the search process. Fitness comparisons among solutions in single-objective optimization is straightforward, but when dealing with multiple objectives, it becomes a non-trivial task. Pareto dominance has been the most commonly adopted relation to compare solutions in a multiobjective optimization context. However, it has been shown that as the number of objectives increases, the convergence ability of approaches based on Pareto dominance decreases. In this paper, we propose three novel fitness assignment methods for many-objective optimization. We also perform a comparative study in order to investigate how effective are the proposed approaches to guide the search in high-dimensional objective spaces. Results indicate that our approaches behave better than six state-of-the-art fitness assignment methods.
BibTeX
@inproceedings{Fabre09,
author = {Mario {Garza Fabre} and Gregorio {Toscano Pulido} and Carlos A. {Coello Coello}},
title = {{Ranking Methods for Many-Objective Problems}},
booktitle = {MICAI 2009: Advances in Artificial Intelligence. 8th Mexican International Conference on Artificial Intelligence},
editor = {Arturo Hernandez Aguirre and Raul Monroy Borja and Carlos Alberto Reyes Garcia},
pages = {633--645},
publisher = {Springer. Lecture Notes in Artificial Intelligence Vol. 5845},
address = {Guanajuato, Mexico},
month = {November},
year = {2009},
doi = {10.1007/978-3-642-05258-3_56},
url = {https://doi.org/10.1007/978-3-642-05258-3_56},
isbn = {978-3-642-05257-6},
}