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More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems.

, , , , , , and . SSCI, page 1-7. IEEE, (2021)

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Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial optimization., , , and . CEC, page 1-8. IEEE, (2020)A Multifactorial Cellular Genetic Algorithm for Multimodal Multitask Optimization., , , and . CEC, page 1-8. IEEE, (2022)Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges., , , , , , , and . Inf. Fusion, (2021)Collaborative Exploration and Reinforcement Learning between Heterogeneously Skilled Agents in Environments with Sparse Rewards., , , and . IJCNN, page 1-10. IEEE, (2021)Hybridizing differential evolution and novelty search for multimodal optimization problems., , , , , and . GECCO (Companion), page 1980-1989. ACM, (2019)On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking., , , , and . ITSC, page 1-8. IEEE, (2020)dMFEA-II: An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems., , , , and . GECCO Companion, page 1690-1696. ACM, (2020)MO-MFCGA: Multiobjective Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking., , , , , and . SSCI, page 1-8. IEEE, (2021)Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis., , , , and . CEC, page 1-8. IEEE, (2020)EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks., , , , and . Neural Networks, (January 2023)