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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. (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.

Toward the coevolution of novel vertical-axis wind turbines (2015)
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

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 full potential. I... Read More about Toward the coevolution of novel vertical-axis wind turbines.

Towards the evolution of vertical-axis wind turbines using supershapes (2014)
Journal Article
Preen, R. J., 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.

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system (2014)
Journal Article
Preen, R. J., 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.

Towards the evolution of novel vertical-axis wind turbines (2013)
Presentation / Conference
Preen, R., & Bull, L. (2013, September). Towards the evolution of novel vertical-axis wind turbines. Paper presented at 13th UK Workshop on Computational Intelligence, UKCI 2013

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 still remains a long way from reaching its full potential. In this paper... Read More about Towards the evolution of novel vertical-axis wind turbines.

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.

Fuzzy dynamical genetic programming in XCSF (2011)
Conference Proceeding
Preen, R. J., Preen, R., & Bull, L. (2011). Fuzzy dynamical genetic programming in XCSF. In N. Krasnogor, & P. L. Lanzi (Eds.), Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, (167-168). https://doi.org/10.1145/2001858.2001952

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigat... Read More about Fuzzy dynamical genetic programming in XCSF.

Arithmetic dynamical genetic programming in the XCSF learning classifier system (2011)
Presentation / Conference
Preen, R., & Bull, L. (2011, June). Arithmetic dynamical genetic programming in the XCSF learning classifier system. Paper presented at IEEE Congress on Evolutionary Computation (CEC), 2011

This paper presents results from an investigation into using a continuous-valued dynamical system representation within the XCSF Learning Classifier System. In particular, dynamical arithmetic genetic networks are used to represent the traditional... Read More about Arithmetic dynamical genetic programming in the XCSF learning classifier system.

Identifying trade entry and exit timing using mathematical technical indicators in XCS (2010)
Journal Article
Preen, R. (2010). Identifying trade entry and exit timing using mathematical technical indicators in XCS. Lecture Notes in Artificial Intelligence, 6471 LNAI, 166-184. https://doi.org/10.1007/978-3-642-17508-4_11

This paper extends current LCS research into financial time series forecasting by analysing the performance of agents utilising mathematical technical indicators for both environment classification and in selecting actions to be executed. It compares... Read More about Identifying trade entry and exit timing using mathematical technical indicators in XCS.

On dynamical genetic programming: Random boolean networks in learning classifier systems (2009)
Journal Article
Bull, L., & Preen, R. (2009). On dynamical genetic programming: Random boolean networks in learning classifier systems. Lecture Notes in Artificial Intelligence, 5481 LNCS, 37-48. https://doi.org/10.1007/978-3-642-01181-8_4

Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system... Read More about On dynamical genetic programming: Random boolean networks in learning classifier systems.

Discrete dynamical genetic programming in XCS (2009)
Presentation / Conference
Preen, R., & Bull, L. (2009, July). Discrete dynamical genetic programming in XCS. Paper presented at 11th Annual conference on Genetic and evolutionary computation

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 a discrete dynamical system representati... Read More about Discrete dynamical genetic programming in XCS.

An XCS approach to forecasting financial time series (2009)
Presentation / Conference
Preen, R. (2009, June). An XCS approach to forecasting financial time series. Paper presented at 11th Annual conference on Genetic and evolutionary computation

This paper extends current LCS research into financial time series forecasting by analysing the performance of agents utilising mathematical technical indicators for both environment classification and in selecting actions to be executed in the envir... Read More about An XCS approach to forecasting financial time series.