All Outputs (11)

Autoencoding with a classifier system (2021)
Journal Article
Preen, R. J., Wilson, S. W., & Bull, L. (2021). Autoencoding with a classifier system. IEEE Transactions on Evolutionary Computation, 25(6), 1079 - 1090. https://doi.org/10.1109/TEVC.2021.3079320

Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size com... Read More about Autoencoding with a classifier system.

Towards an evolvable cancer treatment simulator (2019)
Journal Article
Preen, R. J., Bull, L., & Adamatzky, A. (2019). Towards an evolvable cancer treatment simulator. BioSystems, 182, 1-7. https://doi.org/10.1016/j.biosystems.2019.05.005

© 2019 Elsevier B.V. The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies. However, increasing realism comes at the cost of increasing computational requirements... Read More about Towards an evolvable cancer treatment simulator.

Evolutionary n-level hypergraph partitioning with adaptive coarsening (2019)
Journal Article
Preen, R., & Smith, J. (2019). Evolutionary n-level hypergraph partitioning with adaptive coarsening. IEEE Transactions on Evolutionary Computation, 23(6), 962-971. https://doi.org/10.1109/TEVC.2019.2896951

Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current techniques use a multilevel appro... Read More about Evolutionary n-level hypergraph partitioning with adaptive coarsening.

Design mining microbial fuel cell cascades (2018)
Journal Article
Preen, R., You, J., Bull, L., & Ieropoulos, I. A. (2019). Design mining microbial fuel cell cascades. Soft Computing, 23(13), 4673-7643. https://doi.org/10.1007/s00500-018-3117-x

Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms. For practical applications, it has been suggested that greater efficiency can be achieved by arranging... Read More about Design mining microbial fuel cell cascades.

On Design Mining: Coevolution and Surrogate Models (2017)
Journal Article
Preen, R., & Bull, L. (2017). On Design Mining: Coevolution and Surrogate Models. Artificial Life, 23(2), 186-205. https://doi.org/10.1162/ARTL_a_00225

© 2017 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of ph... Read More about On Design Mining: Coevolution and Surrogate Models.

3D printed components of microbial fuel cells: Towards monolithic microbial fuel cell fabrication using additive layer manufacturing (2016)
Journal Article
Preen, R. J., You, J., Preen, R., Bull, L., Greenman, J., & Ieropoulos, I. (2017). 3D printed components of microbial fuel cells: Towards monolithic microbial fuel cell fabrication using additive layer manufacturing. Sustainable Energy Technologies and Assessments, 19, 94-101. https://doi.org/10.1016/j.seta.2016.11.006

© 2016 The Authors For practical applications of the MFC technology, the design as well as the processes of manufacturing and assembly, should be optimised for the specific target use. Another burgeoning technology, additive manufacturing (3D printin... Read More about 3D printed components of microbial fuel cells: Towards monolithic microbial fuel cell fabrication using additive layer manufacturing.

Design mining interacting wind turbines (2016)
Journal Article
Preen, R. J., Preen, R., & Bull, L. (2016). Design mining interacting wind turbines. Evolutionary Computation, 24(1), 89-111. https://doi.org/10.1162/EVCO_a_00144

© 2016 by the Massachusetts Institute of Technology. An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated... Read More about Design mining interacting wind turbines.

Towards the evolution of vertical-axis wind turbines using supershapes (2014)
Journal Article
Preen, R., & Bull, L. (2014). Towards the evolution of vertical-axis wind turbines using supershapes. Evolutionary Intelligence, 7(3), 155-167. https://doi.org/10.1007/s12065-014-0116-4

© 2014, Springer-Verlag Berlin Heidelberg. We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under fan generated wind conditions after... Read More about Towards the evolution of vertical-axis wind turbines using supershapes.

Toward the Coevolution of Novel Vertical-Axis Wind Turbines (2014)
Journal Article
Preen, R., & Bull, L. (2015). Toward the Coevolution of Novel Vertical-Axis Wind Turbines. IEEE Transactions on Evolutionary Computation, 19(2), 284-294. https://doi.org/10.1109/TEVC.2014.2316199

© 1997-2012 IEEE. The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but remains a long way from reaching its... Read More about Toward the Coevolution of Novel Vertical-Axis Wind Turbines.

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system (2013)
Journal Article
Preen, R., & Bull, L. (2014). Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Computing, 18(1), 153-167. https://doi.org/10.1007/s00500-013-1044-4

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system repr... Read More about Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system.

Dynamical genetic programming in XCSF (2013)
Journal Article
Preen, R. J., Preen, R., & Bull, L. (2013). Dynamical genetic programming in XCSF. Evolutionary Computation, 21(3), 361-387. https://doi.org/10.1162/EVCO_a_00080

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic... Read More about Dynamical genetic programming in XCSF.