解と適応度の共進化（SAFE）：解と目的関数の同時進化
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions
ロボット迷路課題において、解候補と目的関数候補を共進化させるSAFEアルゴリズムを提案し、解だけでなく評価に必要な目的関数も進化的に獲得できることを示した。
著者: Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
分類: cs.NE, cs.RO
原文アブストラクト
We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, \textit{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 \textit{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 \textbf{S}olution \textbf{A}nd \textbf{F}itness \textbf{E}volution (\textbf{SAFE}), a \textit{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.