All Outputs (28)

A haploid-diploid evolutionary algorithm optimizing nanoparticle based cancer treatments (2022)
Book Chapter
Tsompanas, M., Bull, L., Adamatzky, A., & Balaz, I. (2022). A haploid-diploid evolutionary algorithm optimizing nanoparticle based cancer treatments. In I. Balaz, & A. Adamatzky (Eds.), Cancer, Complexity, Computation (237-251). Springer. https://doi.org/10.1007/978-3-031-04379-6_10

This paper uses a recent explanation for the fundamental haploid-diploid lifecycle of eukaryotic organisms to present a new evolutionary algorithm that differs from all previous known work using diploid representations. A form of the Baldwin effect h... Read More about A haploid-diploid evolutionary algorithm optimizing nanoparticle based cancer treatments.

Evolving Boolean regulatory networks with variable gene expression times (2021)
Book Chapter
Bull, L. (2021). Evolving Boolean regulatory networks with variable gene expression times. In Handbook of Unconventional Computing (247-259). World Scientific Publishing. https://doi.org/10.1142/9789811235726_0007

The time taken for gene expression varies not least because proteins vary in length considerably. This paper uses an abstract, tuneable Boolean regulatory network model to explore gene expression time variation. In particular, it is shown how non-uni... Read More about Evolving Boolean regulatory networks with variable gene expression times.

Imitation programming unorganised machines (2013)
Book Chapter
Bull, L. (2013). Imitation programming unorganised machines. In X. Yang (Ed.), Artificial Intelligence, Evolutionary Computing and Metaheuristics: in the Footsteps of Alan Turing (63-81). Springer

In 1948 Alan Turing presented a general representation scheme by which to achieve artificial intelligence – his unorganised machines. Further, at the same time as also suggesting that natural evolution may provide inspiration for search, he noted tha... Read More about Imitation programming unorganised machines.

Toward turing’s A-type unorganised machines in an unconventional substrate: A dynamic representation in compartmentalised excitable chemical media (2013)
Book Chapter
Bull, L., Holley, J., De Lacy Costello, B., & Adamatzky, A. (2013). Toward turing’s A-type unorganised machines in an unconventional substrate: A dynamic representation in compartmentalised excitable chemical media. . Springer. https://doi.org/10.1007/978-3-642-37225-4_11

© Springer-Verlag Berlin Heidelberg 2013. Turing presented a general representation scheme by which to achieve artificial intelligence – unorganised machines. Significantly, these were a form of discrete dynamical system and yet such representations... Read More about Toward turing’s A-type unorganised machines in an unconventional substrate: A dynamic representation in compartmentalised excitable chemical media.

Applications of Kauffman’s coevolutionary NKCS model to management and organization studies (2011)
Book Chapter
Vidgen, R., & Bull, L. (2011). Applications of Kauffman’s coevolutionary NKCS model to management and organization studies. In P. Allen, S. Maguire, & B. McKelvey (Eds.), The Sage Handbook of Complexity and Management (201-219). SAGE Publications Limited

A number of studies have used tuneable, abstract models of organism evolution like Kauffman's (1993) NK model to capture and explore aspects of organizations and their adaptation in the business environment (eg Levinthal, 1997; Rivkin, 2000; Lenox... Read More about Applications of Kauffman’s coevolutionary NKCS model to management and organization studies.

Coevolving cellular automata with memory for chemical computing: Boolean logic gates in the BZ reaction (2008)
Book Chapter
Stone, C., Toth, R., de Lacy Costello, B., Bull, L., & Adamatzky, A. (2008). Coevolving cellular automata with memory for chemical computing: Boolean logic gates in the BZ reaction. In G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, & N. Beume (Eds.), Parallel Problem Solving from Nature - PPSN X: 10th International Conference Dortmund, Germany, September 13-17, 2008 (579-588). Berlin/Heidelberg: Springer

Towards clustering with learning classifier systems (2008)
Book Chapter
Tamee, K., Bull, L., & Pinngern, O. (2008). Towards clustering with learning classifier systems. In L. Bull, E. Bernadó-Mansilla, & J. Holmes (Eds.), Learning classifier systems in data mining (191-204). Springer

Knowledge discovery from medical data: an empirical study with XCS (2008)
Book Chapter
Kharbat, F., Odeh, M., & Bull, L. (2008). Knowledge discovery from medical data: an empirical study with XCS. In L. Bull, E. Bernado-Mansilla, & J. Holmes (Eds.), Learning Classifier Systems in Data Mining. Springer

In this chapter we describe the use of a modern learning classifier system to a data mining task. In particular, in collaboration with a medical specialist, we apply XCS to a primary breast cancer data set. Our results indicate more effective knowled... Read More about Knowledge discovery from medical data: an empirical study with XCS.

Backpropagation in accuracy-based neural learning classifier systems (2007)
Book Chapter
O'Hara, T., & Bull, L. (2007). Backpropagation in accuracy-based neural learning classifier systems. In J. Bacardit, E. Bernadó-Mansilla, M. V. Butz, T. Kovacs, X. Llorà, & K. Takadama (Eds.), Learning Classifier Systems (25-39). Springer Verlag. https://doi.org/10.1007/978-3-540-71231-2_3

Learning Classifier Systems traditionally use a binary string rule representation with wildcards added to allow for generalizations over the problem encoding. We have presented a neural network-based representation to aid their use in complex problem... Read More about Backpropagation in accuracy-based neural learning classifier systems.

Towards machine learning control of chemical computers (2006)
Book Chapter
Budd, A., Stone, C., Masere, J., Adamatzky, A., de Lacy Costello, B., & Bull, L. (2006). Towards machine learning control of chemical computers. In A. Adamatzky, & C. Teuscher (Eds.), From Utopian to Genuine Unconventional Computers (17-36). Frome, UK: Luniver Press

Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network (2005)
Book Chapter
O'Hara, T., & Bull, L. (2005). Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network. In T. O'Hara, & L. Bull (Eds.), The 2005 IEEE Congress on Evolutionary Computation (2046-2052). IEEE. https://doi.org/10.1109/CEC.2005.1554947

An analysis of continuous-valued representations for learning classifier systems (2005)
Book Chapter
Stone, C., & Bull, L. (2005). An analysis of continuous-valued representations for learning classifier systems. In L. Bull, & T. Kovacs (Eds.), Foundations of learning classifier systems (127-178). Springer

Foundations of learning classifier systems: An introduction (2005)
Book Chapter
Bull, L., & Kovacs, T. (2005). Foundations of learning classifier systems: An introduction. In L. Bull, & T. Kovacs (Eds.), Foundations of Learning Classifier Systems (1-17). Springer. https://doi.org/10.1007/11319122_1

A memetic learning classifier system for describing continuous-valued problem spaces (2005)
Book Chapter
Wyatt, D., & Bull, L. (2005). A memetic learning classifier system for describing continuous-valued problem spaces. In W. E. Hart, N. Krasnogor, & J. Smith (Eds.), Recent advances in memetic algorithms (355-395). Springer. https://doi.org/10.1007/3-540-32363-5_15

Two simple learning classifier systems (2005)
Book Chapter
Bull, L. (2005). Two simple learning classifier systems. In L. Bull, & T. Kovacs (Eds.), Foundations of Learning Classifier Systems (913-913). Springer. https://doi.org/10.1007/11319122_4

GAP: Constructing and selecting features with evolutionary computing (2004)
Book Chapter
Smith, M., & Bull, L. (2004). GAP: Constructing and selecting features with evolutionary computing. In A. Ghosh (Ed.), Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing) (41-56). Springer

Learning classifier systems: A brief introduction (2004)
Book Chapter
Bull, L. (2004). Learning classifier systems: A brief introduction. In L. Bull (Ed.), Applications of Learning Classifier Systems (1-12). Springer Berlin/Heidelberg. https://doi.org/10.1007/978-3-540-39925-4_1

Towards distributed adaptive control for road traffic junction signals using learning classifier systems (2004)
Book Chapter
Bull, L., Sha’Aban, J., Tomlinson, A., Addison, J. D., & Heydecker, B. G. (2004). Towards distributed adaptive control for road traffic junction signals using learning classifier systems. In L. Bull (Ed.), Applications of Learning Classifier Systems (276-299). Springer Berlin/Heidelberg. https://doi.org/10.1007/978-3-540-39925-4_12

Building compact rulesets for describing continuous-valued problem spaces using a learning classifier system (2004)
Book Chapter
Wyatt, D., Bull, L., & Parmee, I. (2004). Building compact rulesets for describing continuous-valued problem spaces using a learning classifier system. In I. Parmee (Ed.), Adaptive Computing in Design and Manufacture VI (235-248). Springer

Towards learning classifier systems for continuous-valued online environments (2003)
Book Chapter
Stone, C., & Bull, L. (2003). Towards learning classifier systems for continuous-valued online environments. In E. Cantu-Paz (Ed.), GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: Part II (209-209). Springer