遍历边缘优化
Optimizing at the Ergodic Edge
摘要：
使用一个简单的，退火模型，最近推出的极值优化启发式的一些关键功能进行了演示。特别是，它示出的局部搜索的动力学具有一个通用的临界点，其唯一的参数的变化下，分离阶段太贪婪（非遍历，堵塞）和太随机（遍历）的探索。在这个模型中的各种局部搜索方法的比较表明，临界点的存在是必不可少的启发式的最佳性能。
英文摘要：
Using a simple, annealed model, some of the key features of the recently introduced extremal optimization heuristic are demonstrated. In particular, it is shown that the dynamics of local search possesses a generic critical point under the variation of its sole parameter, separating phases of too greedy (non-ergodic, jammed) and too random (ergodic) exploration. Comparison of various local search methods within this model suggests that the existence of the critical point is essential for the optimal performance of the heuristic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
