On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains
Conference Proceeding
Stone, C., Hart, E., & Paechter, B. (2018)
On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains. In Parallel Problem Solving from Nature – PPSN XV 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part Ihttps://doi.org/10.1007/978-3-319-99253-2_14
Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, rely on a set of domain-specific low-level heuristics at lower levels. For some domains,...
Analysing the performance of migrating birds optimisation approaches for large scale continuous problems
Conference Proceeding
Lalla-Ruiz, E., Segredo, E., Voss, S., Hart, E., & Paechter, B. (2016)
Analysing the performance of migrating birds optimisation approaches for large scale continuous problems. In Parallel Problem Solving from Nature – PPSN XIV. , (134-144). https://doi.org/10.1007/978-3-319-45823-6_13
We present novel algorithmic schemes for dealing with large scale continuous problems. They are based on the recently proposed population-based meta-heuristics Migrating Birds...
Solving CSPs using self-adaptive constraint weights: how to prevent EAs from cheating
Conference Proceeding
Eiben, A. E., Jansen, B., Michalewicz, Z., & Paechter, B. (2000)
Solving CSPs using self-adaptive constraint weights: how to prevent EAs from cheating. In GECCO'00: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation. , (128-134
This paper examines evolutionary algorithms (EAs) extended by various penalty-based approaches to solve constraint satisfaction problems (CSPs). In some approaches, the penalt...
Improving the performance of a preference-based multi-objective algorithm to optimize food treatment processes
Journal Article
Ferrández, M. R., Redondo, J. L., Ivorra, B., Ramos, A. M., Ortigosa, P. M., & Paechter, B. (2020)
Improving the performance of a preference-based multi-objective algorithm to optimize food treatment processes. Engineering Optimization, 52(5), 896-913. https://doi.org/10.1080/0305215x.2019.1618289
This work focuses on the optimization of some high-pressure and temperature food treatments. In some cases, when dealing with real-life multi-objective optimization problems, ...
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...
A framework for distributed evolutionary algorithms.
Conference Proceeding
Arenas, M. G., Collet, P., Eiben, A. E., Jeasity, M., Merelo Guervós, J. J., Paechter, B., …Schoenauer, M. (2002)
A framework for distributed evolutionary algorithms. In 7th International Conference, Granada, Spain, September 7-11, 2002, Proceedings, 665-675
This paper describes the recently released DREAM (Distributed Resource Evolutionary Algorithm Machine) framework for the automatic distribution of evolutionary algorithm (EA) ...
Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm
Conference Proceeding
Steyven, A., Hart, E., & Paechter, B. (2016)
Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. In Parallel Problem Solving from Nature – PPSN XIV; Lecture Notes in Computer Science. , (921-931). https://doi.org/10.1007/978-3-319-45823-6_86
It is well known that in open-ended evolution, the nature of the environment plays in key role in directing evolution. However, in Evolutionary Robotics, it is often unclear e...
Timetabling the classes of an entire university with an evolutionary algorithm.
Conference Proceeding
Paechter, B., Rankin, B., Cumming, A., & Fogarty, T. C. (1998)
Timetabling the classes of an entire university with an evolutionary algorithm. In T. Beck, & M. Schoenauer (Eds.), Parallel Problem Solving from Nature - PPSN V. , (865-874). https://doi.org/10.1007/BFb0056928
This paper describes extensions to an evolutionary algorithm that timetables classes for an entire University. A new method of dealing with multi-objectives is described along...
Multi-Objective Optimisation of the Pump Scheduling Problem using SPEA2.
Conference Proceeding
Lopez-Ibanez, M., Devi Prasad, T., & Paechter, B. (2005)
Multi-Objective Optimisation of the Pump Scheduling Problem using SPEA2. https://doi.org/10.1109/CEC.2005.1554716
Significant operational cost and energy savings can be achieved by optimising the schedules of pumps, which pump water from source reservoirs to storage tanks, in water distri...
A distributed resource evolutionary algorithm machine.
Conference Proceeding
Paechter, B., Baeck, T., Schoenauer, M., Eiben, A. E., Merelo Guervós, J. J., Sebag, M., & Fogarty, T. C. (2002)
A distributed resource evolutionary algorithm machine. In Proceedings of the 2000 Congress on Evolutionary Computation, 2000, 951-958. https://doi.org/10.1109/CEC.2000.870746
This paper describes a project funded by the
European Commission’ which seeks to provide the
technology and software infrastructure necessary to
support the next generation of...