摘要翻译：
启发式搜索方法的自动化设计是计算机科学、人工智能和运筹学中一个活跃的研究领域。为了使这些方法具有更广泛的适用性，在设计求解给定计算搜索问题的有效方法的过程中，消除或减少人类专家的作用是非常重要的。开发这类方法的研究人员往往受到测试其自适应、自配置算法的问题域数量的限制；这可以用实现相应领域特定软件组件的固有困难来解释。本文介绍了HyFlex，一个用于开发跨域搜索方法的软件框架。该框架具有处理不同组合优化问题的通用软件接口，并提供特定于问题的算法组件。这样，算法设计者不需要对问题域有详细的了解，从而可以集中精力设计自适应通用启发式搜索算法。充分实现了四个硬组合问题（最大可满足性、一维装箱、置换流水车间和人员调度），每个问题都包含一组不同的实例数据（包括现实世界的工业应用）和一组广泛的问题特定的启发式和搜索算子。该框架构成了第一个国际跨领域启发式搜索挑战赛的基础，目前正在国际研究界使用。总之，HyFlex代表了启发式搜索通用性的一个有价值的新基准，与之相比，自适应跨域算法可以很容易地开发和可靠地进行比较。
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英文标题：
《HyFlex: A Benchmark Framework for Cross-domain Heuristic Search》
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作者：
Edmund Burke, Tim Curtois, Matthew Hyde, Gabriela Ochoa, Jose A.
  Vazquez-Rodriguez
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最新提交年份：
2011
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分类信息：

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：
  Automating the design of heuristic search methods is an active research field within computer science, artificial intelligence and operational research. In order to make these methods more generally applicable, it is important to eliminate or reduce the role of the human expert in the process of designing an effective methodology to solve a given computational search problem. Researchers developing such methodologies are often constrained on the number of problem domains on which to test their adaptive, self-configuring algorithms; which can be explained by the inherent difficulty of implementing their corresponding domain specific software components.   This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems, and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge the problem domains, and thus can concentrate his/her efforts in designing adaptive general-purpose heuristic search algorithms. Four hard combinatorial problems are fully implemented (maximum satisfiability, one dimensional bin packing, permutation flow shop and personnel scheduling), each containing a varied set of instance data (including real-world industrial applications) and an extensive set of problem specific heuristics and search operators. The framework forms the basis for the first International Cross-domain Heuristic Search Challenge (CHeSC), and it is currently in use by the international research community. In summary, HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed, and reliably compared.
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PDF链接：
https://arxiv.org/pdf/1107.5462