Accelerating neural network architecture search using multi-GPU high-performance computing
Journal Article
Lupión, M., Cruz, N. C., Sanjuan, J. F., Paechter, B., & Ortigosa, P. M. (2023)
Accelerating neural network architecture search using multi-GPU high-performance computing. Journal of Supercomputing, 79, 7609-7625. https://doi.org/10.1007/s11227-022-04960-z
Neural networks stand out from artificial intelligence because they can complete challenging tasks, such as image classification. However, designing a neural network for a par...
On the comparison of initialisation strategies in differential evolution for large scale optimisation
Journal Article
Segredo, E., Paechter, B., Segura, C., & González-Vila, C. I. (2018)
On the comparison of initialisation strategies in differential evolution for large scale optimisation. Optimization Letters, 12(1), 221-234. https://doi.org/10.1007/s11590-017-1107-z
Differential Evolution (DE) has shown to be a promising global opimisation solver for continuous problems, even for those with a large dimensionality. Different previous works...
A Lifelong Learning Hyper-heuristic Method for Bin Packing.
Journal Article
Hart, E., Sim, K., & Paechter, B. (2015)
A Lifelong Learning Hyper-heuristic Method for Bin Packing. Evolutionary Computation, 23(1), 37-67. https://doi.org/10.1162/EVCO_a_00121
We describe a novel Hyper-heuristic system which continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics ...
Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.
Journal Article
Lopez-Ibanez, M., Tumula, P., & Paechter, B. (2011)
Representations and evolutionary operators for the scheduling of pump operations in water distribution networks. Evolutionary Computation, 19, 429-467. https://doi.org/10.1162/EVCO_a_00035
Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operati...
Heaven and Hell: visions for pervasive adaptation
Journal Article
Paechter, B., Pitt, J., Serbedzija, N., Michael, K., Willies, J., & Helgason, I. (2011)
Heaven and Hell: visions for pervasive adaptation. Procedia Computer Science, 7, 81-82. https://doi.org/10.1016/j.procs.2011.12.025
With everyday objects becoming increasingly smart and the “info-sphere” being enriched with nano-sensors and networked to computationally-enabled devices and services, the way...
Setting the research agenda in automated timetabling: the second international timetabling competition
Journal Article
McCollum, B., Schaerf, A., Paechter, B., McMulan, P., Lewis, R. M. R., Parkes, A. J., …Burke, E. (2010)
Setting the research agenda in automated timetabling: the second international timetabling competition. INFORMS Journal on Computing, 22, 120-130. https://doi.org/10.1287/ijoc.1090.0320
The Second International Timetabling Competition (TTC2007) opened in August 2007. Building on the success of the first competition in 2002, this sequel aimed to further develo...
Finding feasible timetables using group-based operators.
Journal Article
Lewis, R. M. R. & Paechter, B. (2007)
Finding feasible timetables using group-based operators. IEEE Transactions on Evolutionary Computation. 11, 397-413. doi:10.1109/TEVC.2006.885162. ISSN 1089-778X
This paper describes the applicability of the so-called "grouping genetic algorithm" to a well-known version of the university course timetabling problem. We note that there a...
A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS
Journal Article
Baños, R., Gil, C., Paechter, B., & Ortega, J. (2007)
A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS. Journal of Mathematical Modelling and Algorithms, 6(2), 213-230. https://doi.org/10.1007/s10852-006-9041-6
Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the mu...
PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation
Journal Article
de Toro Negro, F., Ortega, J., Ros, E., Mota, S., Paechter, B. & Martin, J. M. (2003)
PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation. Parallel Computing. 30, 551-816. doi:10.1016/j.parco.2003.12.012. ISSN 0167-8191
This paper deals with the study of the cooperation between parallel processing and evolutionary computation to obtain efficient procedures for solving multiobjective optimisat...