RL-Based Online Mutation Strategy Selection Techniques inDifferential Evolution: A Study

Title

RL-Based Online Mutation Strategy Selection Techniques inDifferential Evolution: A Study

Creator

Bajpai, Prathu; Bansal, Jagdish Chand

Description

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.

Source

Lecture Notes in Networks and Systems;Volume;1929 LNNS;pp.193-202

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Adaptive Operator Selection Techniques; Differential Evolution; Multi-Arm Bandits; Reinforcement Learning

Coverage

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

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 23673370; ISBN: 978-303222910-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-22911-3_15

https://www.scopus.com/pages/publications/105039269880?origin=resultslist