2020/05/12 by Mudita Sharma, Sharma, Mudita, Manuel López‐Ibáñez +4
Computer Science · Engineering · #Advanced Control Systems Optimization #Advanced Numerical Analysis Techniques #Control Systems and Identification #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #cs.NE
paper · pdf · doi:10.48550/arxiv.2005.05613
arxiv created 2020/05/12 · openalex publication_date 2020/05/12 · arxiv updated 2020/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We conduct an exhaustive survey of adaptive selection of operators (AOS) in Evolutionary Algorithms (EAs). We simplified the AOS structure by adding more components to the framework to built upon the existing categorisation of AOS methods. In addition to simplifying, we looked at the commonality among AOS methods from literature to generalise them. Each component is presented with a number of alternative choices, each represented with a formula. We make three sets of comparisons. First, the methods from literature are tested on the BBOB test bed with their default hyper parameters. Second, the hyper parameters of these methods are tuned using an offline configurator known as IRACE. Third, for a given set of problems, we use IRACE to select the best combination of components and tune their hyper parameters.