Ben Paechter
Ben Paechter

Prof Ben Paechter FBCS CITP

Professor

Biography

Prof. Ben Paechter is Director of Research in the School of Computing. He was Coordinator of the EvoNet, PerAda, and AWARE Coordination Actions within Future and Emerging Technologies (FET) and Deputy Coordinator of the FOCAS Coordination Action. He was a Principal Investigator of the “Speckled Computing” consortium of Scottish universities developing “spray on” computers for wireless sensor networks. He coordinated the FET DREAM project looking at peer-to-peer distribution evolution. He was the scientific officer in charge of the Metaheuristics Network examining the use of metaheuristics for combinatorial optimisation and the FET NEWTIES project which created an artificial society and examined the relationships between individual, social, and evolutionary learning. Prof Paechter is an Associate Editor of “Evolutionary Computation” (MIT Press). He was Joint General Chair of Parallel Problem Solving from Nature (PPSN) 2016.

News

Esteem

Editorial Activity

  • Associate Editor of Evolutionary Computation (MIT Press)

 

Media Activity

  • Timetabling software developed within the Centre for Emergent Computing highlighted in new TEDx talk on Evolutionary Computing and Design
  • PerAda team run successful public engagement event at the Science Museum, London
  • IIDI's AWARE project host successful talk at Edinburgh International Science Festival
  • FoCAS Book: Adaptive Collective Systems - Herding Black Sheep

 

Date


92 results

Learning to solve bin packing problems with an immune inspired hyper-heuristic.

Conference Proceeding
Sim, K., Hart, E., & Paechter, B. (2013)
Learning to solve bin packing problems with an immune inspired hyper-heuristic. In P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds.), Advances in Artificial Life, ECAL 2013, 856-863. https://doi.org/10.7551/978-0-262-31709-2-ch126
Motivated by the natural immune system's ability to defend the body by generating and maintaining a repertoire of antibodies that collectively cover the potential pathogen spa...

A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution.

Conference Proceeding
Sim, K., Hart, E., & Paechter, B. (2012)
A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. In Parallel Problem Solving from Nature: PPSN XII, (348-357). https://doi.org/10.1007/978-3-642-32964-7_35
A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem i...

Introduction to the special section on pervasive adaptation

Journal Article
Zambonelli, F., & Paechter, B. (2012)
Introduction to the special section on pervasive adaptation. ACM transactions on autonomous and adaptive systems, 7(1), 1-2. https://doi.org/10.1145/2168260.2168269

This pervasive day: creative Interactive methods for encouraging public engagement with FET research

Journal Article
Helgason, I., Bradley, J., Egan, C., Paechter, B., & Hart, E. (2011)
This pervasive day: creative Interactive methods for encouraging public engagement with FET research. Procedia Computer Science, 7, 207-208. https://doi.org/10.1016/j.procs.2011.09.028
This paper describes a case study of a programme of interactive public engagement activities presented by the PerAda Co-ordination Action project (FET Proactive Initiative on ...

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...

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...

Towards self-aware PerAda systems.

Conference Proceeding
Hart, E., & Paechter, B. (2010)
Towards self-aware PerAda systems. In E. Hart, C. McEwan, J. Timmis, & A. Hone (Eds.), Artificial Immune Systems: 9th International Conference, ICARIS 2010 Proceedings, 314-216. https://doi.org/10.1007/978-3-642-14547-6_28
Pervasive Adaptation (PerAda) refers to massive-scale pervasive information and communication systems which are capable of autonomously adapting to highly dynamic and open tec...

Strengthening the Forward Variable Selection Stopping Criterion

Conference Proceeding
Herrera, L. J., Rubio, G., Pomares, H., Paechter, B., Guillén, A., & Rojas, I. (2009)
Strengthening the Forward Variable Selection Stopping Criterion. In Artificial Neural Networks – ICANN 2009. , (215-224). https://doi.org/10.1007/978-3-642-04277-5_22
Given any modeling problem, variable selection is a preprocess step that selects the most relevant variables with respect to the output variable. Forward selection is the most...

Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab

Conference Proceeding
Rubio, G., Guillen, A., Pomares, H., Rojas, I., Paechter, B., Glosekotter, P., & Torres-Ceballos, C. I. (2009)
Parallelization of the nearest-neighbour search and the cross-validation error evaluation for the kernel weighted k-nn algorithm applied to large data dets in matlab. In HPCS '09. International Conference on High Performance Computing & Simulation, 2009https://doi.org/10.1109/hpcsim.2009.5192804
The kernel weighted k-nearest neighbours (KWKNN) algorithm is an efficient kernel regression method that achieves competitive results with lower computational complexity than ...

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...

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