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Evolver: Meta-optimizing multi-objective metaheuristics. Aldana Martín, José Francisco Durillo, Juan J. Nebro-Urbaneja, Antonio Jesús Optimización matemática Programación heurística Multi-objective optimization Metaheuristics auto-configuration and auto-design Framework jMetal Evolver is a tool based on the formulation of the automatic configuration and design of multi-objective metaheuristics as a multi-objective optimization problem that can be solved by using the same kind of algorithms; i.e., we are applying a meta-optimization approach. Evolver provides highly configurable implementations of representative multi-objective solvers which can be automatically configured from a number of multi-objective problems used as the training set and a list of quality indicators which are the objectives to be optimized. Our tool is based on the jMetal framework, so a large number of existing algorithms can be used as meta-optimizers. A graphical user interface allows scientists to easily define auto-configuration scenarios, thus simplifying the complex process of finding high-quality algorithm settings. Partial funding for open access: Universidad de Málaga / CBUA 2023-11-13T12:32:55Z 2023-11-13T12:32:55Z 2023-11-13 2023-10-10 journal article VoR Aldana-Martín, J. F., Durillo, J. J., & Nebro, A. J. (2023). Evolver: Meta-optimizing multi-objective metaheuristics. SoftwareX, 24, 101551. https://doi.org/10.1016/j.softx.2023.101551 https://hdl.handle.net/10630/28010 10.1016/j.softx.2023.101551 eng Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ open access application/pdf Elsevier