RT Journal Article
T1 Editorial: 												Memetic Computing: Accelerating optimization heuristics with problem-dependent local search methods
A1 Osaba, Eneko
A1 Del Ser, Javier
A1 Cotta, Carlos
A1 Moscato, Pablo
AB Memetic Algorithms and, in general, approaches underneath the wider Memetic Computing paradigm, have been at the core of a frantic research activity since the very inception of this research area in the late eighties. The community working in this area has so far showcased the benefits of hybridizing population-based algorithms with trajectory-based methods or any other specialized procedures that encompass problem-specific knowledge in a variety of real-world scenarios. From the perspective of the algorithms themselves, this hybridization can be realized in many different ways: it is this upsurge of manifold algorithmic approaches what has maintained a vigorous and intense activity around Memetic Computing over the years, progressively adapting the paradigm to newly emerging problem formulations and characteristics. This editorial introduces the readership of Swarm and Evolutionary Computation to the contributions finally included in the Special Issue on Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods. The high quality of the works presented in this editorial unquestionably proves the excellent health of this vibrant research area, as well as its continued success at tackling challenging real-world optimization problems.
SN 2210-6502
YR 2022
FD 2022-04
LK https://hdl.handle.net/11556/3377
UL https://hdl.handle.net/11556/3377
LA eng
NO Osaba , E , Del Ser , J , Cotta , C & Moscato , P 2022 , ' Editorial : Memetic Computing: Accelerating optimization heuristics with problem-dependent local search methods ' , Swarm and Evolutionary Computation , vol. 70 , 101047 . https://doi.org/10.1016/j.swevo.2022.101047
NO Publisher Copyright: © 2022
NO The guest editors would like to thank Prof. Swagatam Das and Prof. Ponnuthurai Nagaratnam Suganthan, Editors-in-Chief of Swarm and Evolutionary Computation, and their editorial team for their valuable help during the preparation and management of this special issue. We would also like to specially thank the reviewers for the insightful and valuable comments made on the submitted manuscripts, which reflect on the high quality of the finally published articles. Their dedication to this review process during these two pandemic years of 2020 and 2021 is greatly appreciated. Eneko Osaba and Javier Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK funding programs. J. Del Ser also acknowledges support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19). Pablo Moscato’s work was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (project DP200102364) and a generous donation from the Maitland Cancer Appeal. Carlos Cotta acknowledges support by MinEco under project DeepBio (TIN2017-85727-C4-1-P), and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.
DS TECNALIA Publications
RD 28 jul 2024