Hybridizing differential evolution and novelty search for multimodal optimization problems. - Aritz D. Martinez; Eneko Osaba; Izaskun Oregi; Iztok Fister Jr.; Iztok Fister 001; Javier Del Ser (GECCO (Companion), 2019)

Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial optimization. - Aritz D. Martinez; Eneko Osaba; Javier Del Ser; Francisco Herrera (CEC, 2020)

Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis. - Eneko Osaba; Aritz D. Martinez; Jesus L. Lobo; Javier Del Ser; Francisco Herrera (CEC, 2020)

dMFEA-II: An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems. - Eneko Osaba; Aritz D. Martinez; Akemi Gálvez; Andrés Iglesias 001; Javier Del Ser (GECCO Companion, 2020)

On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking. - Eneko Osaba; Aritz D. Martinez; Jesus L. Lobo; Ibai Laña; Javier Del Ser (ITSC, 2020)

Collaborative Exploration and Reinforcement Learning between Heterogeneously Skilled Agents in Environments with Sparse Rewards. - Alain Andres; Esther Villar-Rodriguez; Aritz D. Martinez; Javier Del Ser (IJCNN, 2021)

MO-MFCGA: Multiobjective Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking. - Eneko Osaba; Javier Del Ser; Aritz D. Martinez; Jesus L. Lobo; Antonio J. Nebro; Xin-She Yang 001 (SSCI, 2021)

More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems. - Javier Del Ser; Eneko Osaba; Aritz D. Martinez; Miren Nekane Bilbao; Javier Poyatos; Daniel Molina; Francisco Herrera (SSCI, 2021)

Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions. - Eneko Osaba; Aritz D. Martinez; Javier Del Ser (CoRR, 2021)

Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges. - Aritz D. Martinez; Javier Del Ser; Esther Villar-Rodriguez; Eneko Osaba; Javier Poyatos; Siham Tabik; Daniel Molina; Francisco Herrera (Inf. Fusion, 2021)

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking. - Eneko Osaba; Javier Del Ser; Aritz D. Martinez; Jesus L. Lobo; Francisco Herrera (Inf. Sci., 2021)

A Multifactorial Cellular Genetic Algorithm for Multimodal Multitask Optimization. - Eneko Osaba; Javier Del Ser; Aritz D. Martinez; Jesus L. Lobo (CEC, 2022)

Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions. - Eneko Osaba; Javier Del Ser; Aritz D. Martinez; Amir Hussain 001 (Cogn. Comput., 2022)

Adaptive Multifactorial Evolutionary Optimization for Multitask Reinforcement Learning. - Aritz D. Martinez; Javier Del Ser; Eneko Osaba; Francisco Herrera (IEEE Trans. Evol. Comput., 2022)

EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks. - Javier Poyatos; Daniel Molina; Aritz D. Martinez; Javier Del Ser; Francisco Herrera (Neural Networks, 2023)