Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bajpai, Prathu; Bansal, Jagdish Chand Title A name given to the resource RL-Based Online Mutation Strategy Selection Techniques inDifferential Evolution: A Study Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1929 LNNS;pp.193-202 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-22911-3_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-22911-3_15</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039269880?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039269880?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bajpai P., Department of Statistics and Data Science, School of Sciences, CHRIST (Deemed to be University), Bangalore, India; Bansal J.C., Department of Mathematics, South Asian University, New Delhi, India Description An account of the resource In recent years, reinforcement learning (RL)-based online mutation strategy selection techniques have emerged as a principled learning framework for balancing exploration and exploitation in Differential Evolution. Several state-of-the-art (SOTA) DE variants have been proposed that utilize online mutation strategy selection to improve the performance of the canonical DE algorithm. This paper presents a comprehensive review of such DE variants and studies multi-armed bandit formulations for online mutation strategy selection in DE. It systematically categorizes existing DE variants based on their utilization of RL algorithms. The Author(s) 2026. Subject The topic of the resource Adaptive Operator Selection Techniques; Differential Evolution; Multi-Arm Bandits; Reinforcement Learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303222910-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online