Diversification-based learning in computing and optimization
Diversification-based learning in computing and optimization
复制标题
DOI：
10.1007/s10732-018-9384-y
复制
发表时间：
2017-03
影响因子：
2.7
通讯作者：
F. Glover;Jin-Kao Hao
中科院分区：
文献类型：
--
作者：
F. Glover;Jin-Kao Hao
基于多样化的学习（DBL）源自元分析领域中引入的原理和方法的集合，这些原理和方法在计算和优化中具有广泛的应用。我们表明，DBL框架大大超出了最近的反对为基础的学习（OBL）框架介绍了Tizhoosh（在：2005年，国际建模、控制和自动化计算智能会议和国际智能代理、网络技术和互联网商务会议论文集（CIMCA/IAWTIC-2005），第695-701页），这已经成为机器学习和元启发式优化中的众多研究倡议的焦点。我们统一并扩展了元启发式搜索的早期建议（格洛弗，在Hao J-K，Lutton E，罗纳德E，Schoenauer M，Snyers D（编辑）人工进化，计算机科学讲义，Springer，柏林，第1363卷，第13-54页，1997年;格洛弗和拉古纳塔布搜索，施普林格，柏林，1997年）给出了一个集合的方法，更灵活和全面的比OBL创建集约化和多样化的策略，在元启发式搜索。我们还描述了DBL在机器学习和优化的各个子领域的潜在应用。
Diversification-based learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent opposition-based learning (OBL) framework introduced in Tizhoosh (in: Proceedings of international conference on computational intelligence for modelling, control and automation, and international conference on intelligent agents, web technologies and internet commerce (CIMCA/IAWTIC-2005), pp 695–701, 2005), which has become the focus of numerous research initiatives in machine learning and metaheuristic optimization. We unify and extend earlier proposals in metaheuristic search (Glover, in Hao J-K, Lutton E, Ronald E, Schoenauer M, Snyers D (eds) Artificial evolution, Lecture notes in computer science, Springer, Berlin, vol 1363, pp 13–54, 1997; Glover and Laguna Tabu search, Springer, Berlin, 1997) to give a collection of approaches that are more flexible and comprehensive than OBL for creating intensification and diversification strategies in metaheuristic search. We also describe potential applications of DBL to various subfields of machine learning and optimization.