Generating Human-readable Algorithms for the Travelling Salesman Problem using Hyper-Heuristics
Generating Human-readable Algorithms for the Travelling Salesman Problem using Hyper-Heuristics
复制标题
使用超启发式为旅行商问题生成人类可读的算法
DOI：
10.1145/2739482.2768459
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Shahriar Asta
中科院分区：
文献类型：
--
作者：
Patricia Ryser;J. Miller;Shahriar Asta
超启发式搜索启发式和元启发式的空间，从而可以生成高质量的算法。这是研究界日益感兴趣的领域。算法是使用基于众所周知的启发式和元启发式方法（即迭代局部搜索和模因算法）的“操作模板”迭代构建的。这些超启发式算法选择特定于问题的启发式序列，可以在问题域中找到良好的解决方案。这种“自适应算法”已经解决了几个成熟的组合问题，具有很高的通用性。然而，启发式操作的进化序列通常非常长并且难以理解。在本文中，我们专注于使用创新的自动算法创建方法，在元启发式循环内演化固定的运算符序列。我们在旅行商问题的新独立求解器中提取并硬编码了这些进化算法。
Hyper-heuristics search the space of heuristics and metaheuristics, so that it can generate high-quality algorithms. It is a growing area of interest in the research community. Algorithms have been constructed iteratively using "templates of operations" based on well-known heuristic and metaheuristic methods (i.e. Iterated Local Search and Memetic algorithms). These hyper-heuristic algorithms choose sequences of problem-specific heuristics that can find good solutions in the problem domain. Such "adaptive algorithms" have solved several well-established combinatorial problems, with a high level of generality. However, the evolved sequences of heuristic operations are often very long and defy human comprehension. In this paper, we focus on evolving a fixed sequence of operators inside the loop of a metaheuristic, using an innovative automatic algorithm creation method. We have extracted and hard-coded these evolved algorithms in new independent solvers for Travelling Salesman Problems.
影响因子：
3.6
作者：
Burke, Edmund K.;Gendreau, Michel;Qu, Rong
通讯作者：
Qu, Rong