Divide-and-Evolve：一种新的面向领域无关时间规划的模因机制
Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning
摘要：
提出了一种将演化算法与运筹学方法相结合求解时间规划问题的新方法--分而进化法。标准的Memetic算法使用局部搜索方法来改进进化解，因此当局部方法停止处理完整问题时会失败，而Divide-and-Evolve方法将手头的问题分成几个更容易的子问题，因此可以解决直接输入确定性OR算法时难以解决的全局问题。但是，Divide-and-Evolve方法最突出的优点是，它立即为多目标优化开辟了一条道路，即使使用的OR方法是单目标的。给出了标准（单目标）Zeno交通基准的概念验证方法，并在同一Zeno框架中提出了一个小型的原始多目标基准，以评估所提出的方法的多目标能力，这是时间规划的一个突破。
英文摘要：
An original approach, termed Divide-and-Evolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Problems (TPPs). Whereas standard Memetic Algorithms use local search methods to improve the evolutionary solutions, and thus fail when the local method stops working on the complete problem, the Divide-and-Evolve approach splits the problem at hand into several, hopefully easier, sub-problems, and can thus solve globally problems that are intractable when directly fed into deterministic OR algorithms. But the most prominent advantage of the Divide-and-Evolve approach is that it immediately opens up an avenue for multi-objective optimization, even though the OR method that is used is single-objective. Proof of concept approach on the standard (single-objective) Zeno transportation benchmark is given, and a small original multi-objective benchmark is proposed in the same Zeno framework to assess the multi-objective capabilities of the proposed methodology, a breakthrough in Temporal Planning.
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