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摘要翻译：
破解主谋谜题的艺术由唐纳德·克努斯发起，已经30多岁了；尽管如此，它仍然受到运筹学和计算机游戏期刊的关注，更不用说自然启发的随机算法文献了。在本文中，我们试图提出一种策略，允许自然启发的算法获得与基于穷举搜索策略的算法一样好的结果；为了做到这一点，我们首先回顾、比较和改进当前解决这一难题的方法；然后我们用一个分布估计算法对其中的一个策略进行了测试。最后，我们试图找到一种策略，它不是穷尽的，然后可以包含在自然启发的算法（如进化算法或粒子群算法）中。本文证明了在进化算法的适应度函数中引入局部熵后，进化算法比随机算法具有更好的性能，并给出了如何在不增加计算开销的情况下将最佳启发式策略引入进化算法的经验法则。
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英文标题：
《Adapting Heuristic Mastermind Strategies to Evolutionary Algorithms》
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作者：
Tomas Philip Runarsson, Juan J. Merelo-Guervos
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than 30 years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we try to suggest a strategy that will allow nature-inspired algorithms to obtain results as good as those based on exhaustive search strategies; in order to do that, we first review, compare and improve current approaches to solving the puzzle; then we test one of these strategies with an estimation of distribution algorithm. Finally, we try to find a strategy that falls short of being exhaustive, and is then amenable for inclusion in nature inspired algorithms (such as evolutionary or particle swarm algorithms). This paper proves that by the incorporation of local entropy into the fitness function of the evolutionary algorithm it becomes a better player than a random one, and gives a rule of thumb on how to incorporate the best heuristic strategies to evolutionary algorithms without incurring in an excessive computational cost.
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PDF链接：
https://arxiv.org/pdf/0912.2415
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