Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, conflating the objective with the objective function. Even when the former is well defined, the latter may not be obvious, e.g., in learning a strategy to navigate a maze to find a goal (objective), an effective objective function to evaluate strategies may not be a simple function of the distance to the objective. We proposed to automate the means by which a good objective function may be discovered—a proposal reified herein. We present Solution And Fitness Evolution (SAFE), a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. As proof of principle of this concept, we show that SAFE successfully evolves not only solutions within a robotic maze domain, but also the objective functions needed to measure solution quality during evolution.

Original languageEnglish
Title of host publicationGenetic Programming - 22nd European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Proceedings
EditorsNuno Lourenço, Lukas Sekanina, Ting Hu, Hendrik Richter, Pablo García-Sánchez
PublisherSpringer Cham
Pages146-161
Number of pages16
ISBN (Electronic)978-3-030-16670-0
ISBN (Print)978-3-030-16669-4
DOIs
StatePublished - 27 Mar 2019
Event22nd European Conference on Genetic Programming, EuroGP 2019, held as part of EvoStar 2019 - Leipzig, Germany
Duration: 24 Apr 201926 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11451 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd European Conference on Genetic Programming, EuroGP 2019, held as part of EvoStar 2019
Country/TerritoryGermany
CityLeipzig
Period24/04/1926/04/19

Keywords

  • Coevolution
  • Evolutionary computation
  • Novelty search
  • Objective function

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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