关于随机算子,适应度景观和优化启发式性能。

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Brahim Aboutaib, Sébastien Verel, Cyril Fonlupt, Bilel Derbel, Arnaud Liefooghe, Belaïd Ahiod
{"title":"关于随机算子,适应度景观和优化启发式性能。","authors":"Brahim Aboutaib, Sébastien Verel, Cyril Fonlupt, Bilel Derbel, Arnaud Liefooghe, Belaïd Ahiod","doi":"10.1162/evco.a.24","DOIUrl":null,"url":null,"abstract":"<p><p>Stochastic operators are the backbone of many stochastic optimization algorithms. Besides the existing theoretical analysis that analyzes the asymptotic runtime of such algorithms, characterizing their performances using fitness landscapes analysis is far away. The fitness landscape approach proceeds by considering multiple characteristics to understand and explain an optimization algorithm's performance or the difficulty of an optimization problem, and hence would provide a richer explanation. This paper analyzes the fitness landscapes of stochastic operators by focusing on the number of local optima and their relation to the optimization performance. The search spaces of two combinatorial problems are studied, the NK-landscape and the Quadratic Assignment Problem, using binary string-based and permutation-based stochastic operators. The classical bit-flip search operator is considered for binary strings, and a generalization of the deterministic exchange operator for permutation representations is devised. We study small instances, ranging from randomly generated to real-like instances, and large instances from the NK-landscapes. For large instances, we propose using an adaptive walk process to estimate the number of locally optimal solutions. Given that stochastic operators are usually used within the population and single solution-based evolutionary optimization algorithms, we contrasted the performances of the (μ + λ)-EA, and an Iterated Local Search, versus the landscape properties of large size instances of the NK-landscapes. Our analysis shows that characterizing the fitness landscapes induced by stochastic search operators can effectively explain the optimization performances of the algorithms we considered.</p>","PeriodicalId":50470,"journal":{"name":"Evolutionary Computation","volume":" ","pages":"1-27"},"PeriodicalIF":4.6000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On stochastic operators, fitness landscapes, and optimization heuristics performances.\",\"authors\":\"Brahim Aboutaib, Sébastien Verel, Cyril Fonlupt, Bilel Derbel, Arnaud Liefooghe, Belaïd Ahiod\",\"doi\":\"10.1162/evco.a.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Stochastic operators are the backbone of many stochastic optimization algorithms. Besides the existing theoretical analysis that analyzes the asymptotic runtime of such algorithms, characterizing their performances using fitness landscapes analysis is far away. The fitness landscape approach proceeds by considering multiple characteristics to understand and explain an optimization algorithm's performance or the difficulty of an optimization problem, and hence would provide a richer explanation. This paper analyzes the fitness landscapes of stochastic operators by focusing on the number of local optima and their relation to the optimization performance. The search spaces of two combinatorial problems are studied, the NK-landscape and the Quadratic Assignment Problem, using binary string-based and permutation-based stochastic operators. The classical bit-flip search operator is considered for binary strings, and a generalization of the deterministic exchange operator for permutation representations is devised. We study small instances, ranging from randomly generated to real-like instances, and large instances from the NK-landscapes. For large instances, we propose using an adaptive walk process to estimate the number of locally optimal solutions. Given that stochastic operators are usually used within the population and single solution-based evolutionary optimization algorithms, we contrasted the performances of the (μ + λ)-EA, and an Iterated Local Search, versus the landscape properties of large size instances of the NK-landscapes. Our analysis shows that characterizing the fitness landscapes induced by stochastic search operators can effectively explain the optimization performances of the algorithms we considered.</p>\",\"PeriodicalId\":50470,\"journal\":{\"name\":\"Evolutionary Computation\",\"volume\":\" \",\"pages\":\"1-27\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1162/evco.a.24\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1162/evco.a.24","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

随机算子是许多随机优化算法的基础。除了现有的分析算法渐近运行时的理论分析外,用适应度景观分析来表征其性能还很遥远。适应度景观方法通过考虑多个特征来理解和解释优化算法的性能或优化问题的难度,从而提供更丰富的解释。本文从局部最优的数量及其与优化性能的关系出发,分析了随机算子的适应度格局。利用基于二进制字符串和基于置换的随机算子,研究了两个组合问题的搜索空间,即nk景观和二次分配问题。考虑了二进制字符串的经典位翻转搜索算子,并对置换表示的确定性交换算子进行了推广。我们研究小的实例,从随机生成的到真实的实例,以及来自nk景观的大实例。对于大型实例,我们建议使用自适应行走过程来估计局部最优解的数量。考虑到随机算子通常在种群和基于单解的进化优化算法中使用,我们比较了(μ + λ)-EA和迭代局部搜索的性能与大型nk -景观实例的景观特性。我们的分析表明,表征随机搜索算子引起的适应度景观可以有效地解释我们所考虑的算法的优化性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On stochastic operators, fitness landscapes, and optimization heuristics performances.

Stochastic operators are the backbone of many stochastic optimization algorithms. Besides the existing theoretical analysis that analyzes the asymptotic runtime of such algorithms, characterizing their performances using fitness landscapes analysis is far away. The fitness landscape approach proceeds by considering multiple characteristics to understand and explain an optimization algorithm's performance or the difficulty of an optimization problem, and hence would provide a richer explanation. This paper analyzes the fitness landscapes of stochastic operators by focusing on the number of local optima and their relation to the optimization performance. The search spaces of two combinatorial problems are studied, the NK-landscape and the Quadratic Assignment Problem, using binary string-based and permutation-based stochastic operators. The classical bit-flip search operator is considered for binary strings, and a generalization of the deterministic exchange operator for permutation representations is devised. We study small instances, ranging from randomly generated to real-like instances, and large instances from the NK-landscapes. For large instances, we propose using an adaptive walk process to estimate the number of locally optimal solutions. Given that stochastic operators are usually used within the population and single solution-based evolutionary optimization algorithms, we contrasted the performances of the (μ + λ)-EA, and an Iterated Local Search, versus the landscape properties of large size instances of the NK-landscapes. Our analysis shows that characterizing the fitness landscapes induced by stochastic search operators can effectively explain the optimization performances of the algorithms we considered.

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
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
×
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