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
  • FoCAS Book: Adaptive Collective Systems - Herding Black Sheep
  • IIDI's AWARE project host successful talk at Edinburgh International Science Festival
  • PerAda team run successful public engagement event at the Science Museum, London

 

Date


82 results

Evolving planar mechanisms for the conceptual stage of mechanical design

Conference Proceeding
Lapok, P., Lawson, A., & Paechter, B. (2019)
Evolving planar mechanisms for the conceptual stage of mechanical design. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, (383–384). https://doi.org/10.1145/3319619.3322006
This study presents a method to evolve planar mechanism prototypes using an evolutionary computing approach. Ultimately, the idea is to provide drafts for designers at the con...

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, ...

2-Dimensional Outline Shape Representation for Generative Design with Evolutionary Algorithms

Conference Proceeding
Lapok, P., Lawson, A., & Paechter, B. (2018)
2-Dimensional Outline Shape Representation for Generative Design with Evolutionary Algorithms. In H. Rodrigues, J. Herskovits, C. Mota Soares, A. Araújo, J. Guedes, J. Folgado, …J. Madeira (Eds.), EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization, 926-937. doi:10.1007/978-3-319-97773-7_80
In this paper, we investigate the ability of genetic representation methods to describe two-dimensional outline shapes, in order to use them in a generative design system. A s...

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,...

Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm

Conference Proceeding
Hart, E., Steyven, A. S. W., & Paechter, B. (2018)
Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm. In GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference, (101-108). https://doi.org/10.1145/3205455.3205481
The presence of functionality diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad...

Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs

Conference Proceeding
Stone, C., Hart, E., & Paechter, B. (2017)
Automatic Generation of Constructive Heuristics for Multiple Types of Combinatorial Optimisation Problems with Grammatical Evolution and Geometric Graphs. In Applications of Evolutionary Computation, 578-593
In many industrial problem domains, when faced with a combinatorial optimisation problem, a “good enough, quick enough” solution to a problem is often required. Simple heurist...

Evaluation of a genetic representation for outline shapes

Conference Proceeding
Lapok, P., Lawson, A., & Paechter, B. (2017)
Evaluation of a genetic representation for outline shapes. In GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, (1419-1422). https://doi.org/10.1145/3067695.3082501
This work in progress focuses on the evaluation of a genetic representation for outline shapes for planar mechanical levers which addresses the first stage of the complex real...

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...

An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics

Conference Proceeding
Steyven, A., Hart, E., & Paechter, B. (2017)
An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics. In GECCO '17 Proceedings of the Genetic and Evolutionary, (155-162). https://doi.org/10.1145/3071178.3071232
A robotic swarm that is required to operate for long periods in a potentially unknown environment can use both evolution and individual learning methods in order to adapt. How...

Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation

Conference Proceeding
Segredo, E., Lalla-Ruiz, E., Hart, E., Paechter, B., & Voß, S. (2016)
Hybridisation of Evolutionary Algorithms through hyper-heuristics for global continuous optimisation. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016, (296-305). https://doi.org/10.1007/978-3-319-50349-3_25
Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic whic...

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