{"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}
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.
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.