Damla KizilayM. Fatih TasgetirenHande OztopLevent KandillerPonnuthurai Nagaratnam SuganthanKizilay, DamlaTasgetiren, M. FatihSuganthan, P. N.Oztop, HandeKandiller, Levent2025-10-062020978172816929310.1109/CEC48606.2020.91857432-s2.0-85092034984https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092034984&doi=10.1109%2FCEC48606.2020.9185743&partnerID=40&md5=78e2bac24cf3e1bff903f2be8524b110https://gcris.yasar.edu.tr/handle/123456789/9184https://doi.org/10.1109/CEC48606.2020.9185743In this paper a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known the performance of a DE algorithm depends on the mutation strategy and its control parameters namely crossover and mutation rates. For this reason the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore a new mutation operator namely DE/Best to current/l is proposed in the DE-QL algorithm. In this paper 57 EDPs provided in 'Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results' are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDifferential Evolution, Engineering Design Problems, Epsilon Constraint Handling Method, Reinforcement Learning, Constrained Optimization, Learning Algorithms, Reinforcement Learning, Constrained Optimi-zation Problems, Constraint Handling, Crossover And Mutation, Differential Evolution Algorithms, Engineering Design Problems, Evaluation Criteria, Evolutionary Process, Mutation Operators, Genetic AlgorithmsConstrained optimization, Learning algorithms, Reinforcement learning, Constrained optimi-zation problems, Constraint handling, Crossover and mutation, Differential evolution algorithms, Engineering design problems, Evaluation criteria, Evolutionary process, Mutation operators, Genetic algorithmsDifferential EvolutionEpsilon Constraint Handling MethodEngineering Design ProblemsReinforcement LearningConference Object