论文标题
部分可观测时空混沌系统的无模型预测
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions
论文作者
论文摘要
最近，我们强调了一个基本问题，该问题被认为是混淆算法优化的，即\ textit {Confing}与目标函数的目标。即使对前者的定义很好，后者也可能并不明显，例如，在学习一个策略以浏览迷宫以找到目标（客观）时，\ textit {评估}策略的有效目标函数可能并不是距离目标距离的简单功能。我们建议自动化可以发现良好目标功能的手段 - 此处得到了建议。 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.作为此概念原理的证明，我们表明安全不仅成功地发展了机器人迷宫领域内的解决方案，而且还可以在进化过程中测量解决方案质量所需的目标函数。
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.
