Learning fine-grained search space pruning and heuristics for combinatorial optimization

IF 1.1 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Heuristics Pub Date : 2020-01-05 DOI:10.1007/s10732-023-09512-z

Juho Lauri, Sourav Dutta, Marco Grassia, Deepak Ajwani

引用次数: 10

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用于组合优化的细粒度搜索空间修剪和启发式学习

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Heuristics 工程技术-计算机：理论方法

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

6 months

期刊介绍： The Journal of Heuristics provides a forum for advancing the state-of-the-art in the theory and practical application of techniques for solving problems approximately that cannot be solved exactly. It fosters the development, understanding, and practical use of heuristic solution techniques for solving business, engineering, and societal problems. It considers the importance of theoretical, empirical, and experimental work related to the development of heuristics. The journal presents practical applications, theoretical developments, decision analysis models that consider issues of rational decision making with limited information, artificial intelligence-based heuristics applied to a wide variety of problems, learning paradigms, and computational experimentation. Officially cited as: J Heuristics Provides a forum for advancing the state-of-the-art in the theory and practical application of techniques for solving problems approximately that cannot be solved exactly. Fosters the development, understanding, and practical use of heuristic solution techniques for solving business, engineering, and societal problems. Considers the importance of theoretical, empirical, and experimental work related to the development of heuristics.