Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization Rodriguez-Fernandez, Angel E. Schäpermeier, Lennart Hernández, Carlos Kerschke, Pascal Trautmann, Heike Schütze, Oliver Optimization Evolutionary computation Approximation algorithms Benchmark testing Vectors Surveys Pareto optimization multi-objective optimization evolutionary computation multimodal optimization local solutions 2024 info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_6501 https://ris.uni-paderborn.de/record/56221 Rodriguez-Fernandez AE, Schäpermeier L, Hernández C, Kerschke P, Trautmann H, Schütze O. Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization. <i>IEEE Transactions on Evolutionary Computation</i>. Published online 2024:1-1. doi:<a href="https://doi.org/10.1109/TEVC.2024.3458855">10.1109/TEVC.2024.3458855</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1109/TEVC.2024.3458855 info:eu-repo/semantics/closedAccess