@inproceedings{d6269e4a515e4674ba04874802945aa1,
title = "Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems",
abstract = "We have recently presented SAFE - Solution And Fitness Evolution - a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. We showed that SAFE was successful at evolving solutions within a robotic maze domain. Herein we present an investigation of SAFE's adaptation and application to multiobjective problems, wherein candidate objective functions explore different weightings of each objective. Though preliminary, the results suggest that SAFE, and the concept of coevolving solutions and objective functions, can identify a similar set of optimal multiobjective solutions without explicitly employing a Pareto front for fitness calculation and parent selection. These findings support our hypothesis that the SAFE algorithm concept can not only solve complex problems, but can adapt to the challenge of problems with multiple objectives.",
keywords = "coevolution, evolutionary computation, multiobjective optimization, novelty search, objective function",
author = "Moshe Sipper and Moore, \{Jason H.\} and Urbanowicz, \{Ryan J.\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Congress on Evolutionary Computation, CEC 2019 ; Conference date: 10-06-2019 Through 13-06-2019",
year = "2019",
month = jun,
day = "1",
doi = "10.1109/CEC.2019.8790274",
language = "English",
series = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1868--1874",
booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",
address = "United States",
}