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启发式算法 元启发式算法 超启发式算法 区别 是什么
启发式算法 (Heuristic Algorithms) 是基于直观或经验构造的算法，在可接受的花费 (指计算时间、占用空间等) 下给出待解决组合优化问题每一个实例的一个可行解，该可行解与最优解的偏离程度不一定事先可以预计。 元启发式算法 (Meta-Heuristic Algorithms) 是启发式算法的改进，通常A* star A星搜索 reopen/revisit state
Admissibility + A* with reopen we can derive that we could find the optimal path。 Reopening is what happens when we find a new, better path to a previously expanded node. This is a potentially confusing point because admissibility guarantees for the goal文献阅读--A Machine Learning Based Splitting Heuristic for Divide-and-Conquer Solvers
A Machine Learning Based Splitting Heuristic for Divide-and-Conquer Solvers Nejati S., Le Frioux L., Ganesh V. (2020) A Machine Learning Based Splitting Heuristic for Divide-and-Conquer Solvers. In: Simonis H. (eds) Principles and Practice of Constraint P决策变元选择_决策分支策略——文献学习Exponential Recency Weighted Average Branching Heuristic for SAT Solvers
Exponential Recency Weighted Average Branching Heuristic for SAT Solvers Jia Hui Liang, Vijay Ganesh, Pascal Poupart,等. Exponential Recency Weighted Average Branching Heuristic for SAT Solvers[C]// Thirtieth Aaai Conference on Artificial Intelligence. A决策变元选择_决策分支策略——文献学习An Empirical Study of Branching Heuristics Through the Lens of Global Learning Ra
An Empirical Study of Branching Heuristics Through the Lens of Global Learning Rate Liang J.H., V.K. H.G., Poupart P., Czarnecki K., Ganesh V. (2017) An Empirical Study of Branching Heuristics Through the Lens of Global Learning Rate. In: Gaspers S., Wals