分子设计的自适应多目标进化算法

Christos C. Kannas, C. Pattichis
{"title":"分子设计的自适应多目标进化算法","authors":"Christos C. Kannas, C. Pattichis","doi":"10.1109/CBMS.2017.129","DOIUrl":null,"url":null,"abstract":"Self-adaptation is an efficient way to control the strategy parameters of an Evolutionary Algorithm automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven to work well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed Self-Adaptive Multi-Objective Evolutionary Algorithm is a two level algorithm. The proposed solution is applied on the problem of de novo molecular design. The outer level is the algorithm that is responsible for the self adaptive techniques and is based on Multi-Objective Genetic Algorithm. The inner level is based on the elite Multi-Objective Evolutionary Graph Algorithm. Both the outer and inner algorithms are variations of our previously proposed Multi-Objective Evolutionary Graph Algorithm framework. The outer Multi-Objective Genetic Algorithm operates on a chromosome of elements, while the inner elite Multi-Objective Evolutionary Graph Algorithm operates on molecular graph chromosomes. In general, the proposed solution: (i) searches a larger space, (ii) generates far more solutions per iteration, (iii) evaluates different sets of parameter options for the given problem, and (iv) proposes the fittest parameter sets that should be used for the given problem.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"368 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Self-Adaptive Multi-objective Evolutionary Algorithm for Molecular Design\",\"authors\":\"Christos C. Kannas, C. Pattichis\",\"doi\":\"10.1109/CBMS.2017.129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Self-adaptation is an efficient way to control the strategy parameters of an Evolutionary Algorithm automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven to work well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed Self-Adaptive Multi-Objective Evolutionary Algorithm is a two level algorithm. The proposed solution is applied on the problem of de novo molecular design. The outer level is the algorithm that is responsible for the self adaptive techniques and is based on Multi-Objective Genetic Algorithm. The inner level is based on the elite Multi-Objective Evolutionary Graph Algorithm. Both the outer and inner algorithms are variations of our previously proposed Multi-Objective Evolutionary Graph Algorithm framework. The outer Multi-Objective Genetic Algorithm operates on a chromosome of elements, while the inner elite Multi-Objective Evolutionary Graph Algorithm operates on molecular graph chromosomes. In general, the proposed solution: (i) searches a larger space, (ii) generates far more solutions per iteration, (iii) evaluates different sets of parameter options for the given problem, and (iv) proposes the fittest parameter sets that should be used for the given problem.\",\"PeriodicalId\":141105,\"journal\":{\"name\":\"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)\",\"volume\":\"368 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2017.129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2017.129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

自适应是进化算法在优化过程中自动控制策略参数的一种有效方法。该方法基于策略参数空间的隐式进化搜索,并被证明可以很好地用于从局部到全局的各种策略参数的在线参数控制。我们提出的自适应多目标进化算法是一个两级算法。提出的解决方案应用于从头分子设计问题。外层是基于多目标遗传算法的自适应算法。内部层次是基于精英多目标进化图算法。外部和内部算法都是我们之前提出的多目标进化图算法框架的变体。外部多目标遗传算法是对元素染色体进行操作,而内部精英多目标进化图算法是对分子图染色体进行操作。一般来说,建议的解决方案:(i)搜索更大的空间,(ii)每次迭代生成更多的解决方案,(iii)评估给定问题的不同参数选项集,以及(iv)提出应该用于给定问题的最适合的参数集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Adaptive Multi-objective Evolutionary Algorithm for Molecular Design
Self-adaptation is an efficient way to control the strategy parameters of an Evolutionary Algorithm automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven to work well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed Self-Adaptive Multi-Objective Evolutionary Algorithm is a two level algorithm. The proposed solution is applied on the problem of de novo molecular design. The outer level is the algorithm that is responsible for the self adaptive techniques and is based on Multi-Objective Genetic Algorithm. The inner level is based on the elite Multi-Objective Evolutionary Graph Algorithm. Both the outer and inner algorithms are variations of our previously proposed Multi-Objective Evolutionary Graph Algorithm framework. The outer Multi-Objective Genetic Algorithm operates on a chromosome of elements, while the inner elite Multi-Objective Evolutionary Graph Algorithm operates on molecular graph chromosomes. In general, the proposed solution: (i) searches a larger space, (ii) generates far more solutions per iteration, (iii) evaluates different sets of parameter options for the given problem, and (iv) proposes the fittest parameter sets that should be used for the given problem.
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