基于多臂强盗的多模态优化重启策略改进

A. Dubois, J. Dehos, F. Teytaud
{"title":"基于多臂强盗的多模态优化重启策略改进","authors":"A. Dubois, J. Dehos, F. Teytaud","doi":"10.1109/ICMLA.2018.00057","DOIUrl":null,"url":null,"abstract":"Multi-Modal Optimization problems are widespread and can be solved using numerous methods, such as niching, sharing or clearing. In this paper, we are interested in algorithms based on restart strategies, where the searching point is restarted at another initial position when an optimum is found. Previous works show that the choice of these initial positions greatly impacts the performance of the algorithm but is not easy to make. In this paper, we propose a new restart strategy, based on reinforcement learning. Our algorithm subdivides the search space and uses a Multi-Armed Bandit technique to choose the successive restart positions. We experiment this algorithm on various functions and on a modified Hump function with more complex local areas. Our results show significant improvements over previous algorithms, such as the Quasi-Random restart with Decreasing Step-size algorithm.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"14 1","pages":"338-343"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Improving Multi-modal Optimization Restart Strategy Through Multi-armed Bandit\",\"authors\":\"A. Dubois, J. Dehos, F. Teytaud\",\"doi\":\"10.1109/ICMLA.2018.00057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-Modal Optimization problems are widespread and can be solved using numerous methods, such as niching, sharing or clearing. In this paper, we are interested in algorithms based on restart strategies, where the searching point is restarted at another initial position when an optimum is found. Previous works show that the choice of these initial positions greatly impacts the performance of the algorithm but is not easy to make. In this paper, we propose a new restart strategy, based on reinforcement learning. Our algorithm subdivides the search space and uses a Multi-Armed Bandit technique to choose the successive restart positions. We experiment this algorithm on various functions and on a modified Hump function with more complex local areas. Our results show significant improvements over previous algorithms, such as the Quasi-Random restart with Decreasing Step-size algorithm.\",\"PeriodicalId\":6533,\"journal\":{\"name\":\"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)\",\"volume\":\"14 1\",\"pages\":\"338-343\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2018.00057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2018.00057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

多模态优化问题是一个广泛存在的问题,可以使用多种方法来解决,如小生境、共享或清理。在本文中,我们感兴趣的是基于重新开始策略的算法,当找到最优时,搜索点在另一个初始位置重新开始。先前的研究表明,这些初始位置的选择对算法的性能有很大的影响,但不容易做出选择。在本文中,我们提出了一种新的基于强化学习的重启策略。该算法对搜索空间进行细分,并使用多臂班迪技术选择连续的重启位置。我们在不同的函数和具有更复杂局部区域的改进的驼峰函数上进行了实验。我们的研究结果表明,与以前的算法相比,如减少步长的准随机重启算法有了显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Multi-modal Optimization Restart Strategy Through Multi-armed Bandit
Multi-Modal Optimization problems are widespread and can be solved using numerous methods, such as niching, sharing or clearing. In this paper, we are interested in algorithms based on restart strategies, where the searching point is restarted at another initial position when an optimum is found. Previous works show that the choice of these initial positions greatly impacts the performance of the algorithm but is not easy to make. In this paper, we propose a new restart strategy, based on reinforcement learning. Our algorithm subdivides the search space and uses a Multi-Armed Bandit technique to choose the successive restart positions. We experiment this algorithm on various functions and on a modified Hump function with more complex local areas. Our results show significant improvements over previous algorithms, such as the Quasi-Random restart with Decreasing Step-size algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信