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In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times (2020)
Journal Article
Tsompanas, M. A., Bull, L., Adamatzky, A., & Balaz, I. (2020). In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times. Computer Methods and Programs in Biomedicine, 200, Article 105886. https://doi.org/10.1016/j.cmpb.2020.105886

© 2020 The Author(s) Background and Objective: Cancer tumors constitute a complicated environment for conventional anti-cancer treatments to confront, so solutions with higher complexity and, thus, robustness to diverse conditions are required. Alter... Read More about In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times.

Novelty search employed into the development of cancer treatment simulations (2020)
Journal Article
Tsompanas, M., Bull, L., Adamatzky, A., & Balaz, I. (2020). Novelty search employed into the development of cancer treatment simulations. Informatics in Medicine Unlocked, 19, 100347. https://doi.org/10.1016/j.imu.2020.100347

Conventional optimization methodologies may be hindered when the automated search is stuck into local optima because of a deceptive objective function landscape. Consequently, open ended search methodologies, such as novelty search, have been propose... Read More about Novelty search employed into the development of cancer treatment simulations.

Towards a Physarum learning chip (2016)
Journal Article
Whiting, J. G. H., Jones, J., Bull, L., Levin, M., & Adamatzky, A. (2016). Towards a Physarum learning chip. Scientific Reports, 6, Article 19948. https://doi.org/10.1038/srep19948

Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants... Read More about Towards a Physarum learning chip.

Evolving spiking networks with variable resistive memories (2014)
Journal Article
Howard, G., Bull, L., de Lacy Costello, B., Gale, E., & Adamatzky, A. (2014). Evolving spiking networks with variable resistive memories. Evolutionary Computation, 22(1), 79-103. https://doi.org/10.1162/EVCO_a_00103

Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural n... Read More about Evolving spiking networks with variable resistive memories.

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.

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.

Evolving boolean networks on tunable fitness landscapes (2012)
Journal Article
Bull, L. (2012). Evolving boolean networks on tunable fitness landscapes. IEEE Transactions on Evolutionary Computation, 16(6), 817-828. https://doi.org/10.1109/TEVC.2011.2173578

This paper presents an abstract, tunable model by which to explore aspects of artificial genetic regulatory networks and their design by simulated evolution. The random Boolean network formalism is combined with the NK and $NKCS$ models of fitness la... Read More about Evolving boolean networks on tunable fitness landscapes.

Evolution of plastic learning in spiking networks via memristive connections (2012)
Journal Article
Howard, G., Howard, D., Gale, E., Bull, L., De Lacy Costello, B., & Adamatzky, A. (2012). Evolution of plastic learning in spiking networks via memristive connections. IEEE Transactions on Evolutionary Computation, 16(5), 711-729. https://doi.org/10.1109/TEVC.2011.2170199

This paper presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowi... Read More about Evolution of plastic learning in spiking networks via memristive connections.

Creating unorganised machines from memristors (2012)
Presentation / Conference
Howard, G. D., Bull, L., Costello, B. D. L., & Adamatzky, A. (2012, September). Creating unorganised machines from memristors. Paper presented at ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics, Kos, Greece

On architectures of circuits implemented in simulated Belousov-Zhabotinsky droplets (2012)
Journal Article
Adamatzky, A., Holley, J., Dittrich, P., Gorecki, J., De Lacy Costello, B., Zauner, K. P., & Bull, L. (2012). On architectures of circuits implemented in simulated Belousov-Zhabotinsky droplets. BioSystems, 109(1), 72-77. https://doi.org/10.1016/j.biosystems.2011.12.007

When lipid vesicles filled with Belousov-Zhabotinsky (BZ) excitable chemical medium are packed in tight assembles, waves of excitation may travel between the vesicles. When several waves meet in a vesicle some fragments may deflect, others can annihi... Read More about On architectures of circuits implemented in simulated Belousov-Zhabotinsky droplets.

Production system rules as protein complexes from genetic regulatory networks: An initial study (2012)
Journal Article
Bull, L. (2012). Production system rules as protein complexes from genetic regulatory networks: An initial study. Evolutionary Intelligence, 5(2), 59-67. https://doi.org/10.1007/s12065-012-0078-3

This short paper introduces a new way by which to design production system rules. An indirect encoding scheme is presented which views such rules as protein complexes produced by the temporal behaviour of an artificial genetic regulatory network. Thi... Read More about Production system rules as protein complexes from genetic regulatory networks: An initial study.

Evolution of supershapes for the generation of three-dimensional designs (2012)
Journal Article
Preen, R. J., & Bull, L. (2012). Evolution of supershapes for the generation of three-dimensional designs

This paper explores the evolution of three-dimensional objects with a simple generative encoding, known as the Superformula. Evolving three-dimensional objects has long been of interest in a wide array of disciplines, from engineering (e.g., robotics... Read More about Evolution of supershapes for the generation of three-dimensional designs.

Cartesian genetic programming for memristive logic circuits (2012)
Journal Article
Howard, G. D., Bull, L., & Adamatzky, A. (2012). Cartesian genetic programming for memristive logic circuits. Lecture Notes in Artificial Intelligence, 7244 LNCS, 37-48. https://doi.org/10.1007/978-3-642-29139-5_4

In this paper memristive logic circuits are evolved using Cartesian Genetic Programming. Graphs comprised of implication logic (IMP) nodes are compared to more ubiquitous NAND circuitry on a number of logic circuit problems and a robotic control task... Read More about Cartesian genetic programming for memristive logic circuits.

Using genetical and cultural search to design unorganised machines (2012)
Journal Article
Bull, L. (2012). Using genetical and cultural search to design unorganised machines. Evolutionary Intelligence, 5(1), 23-33. https://doi.org/10.1007/s12065-011-0061-4

In 1948 Turing presented a general representation scheme by which to achieve artificial intelligence-his unorganised machines. Significantly, these were a form of discrete dynamical system and yet dynamical representations remain almost unexplored wi... Read More about Using genetical and cultural search to design unorganised machines.

A simple computational cell: Coupling boolean gene and protein networks (2012)
Journal Article
Bull, L. (2012). A simple computational cell: Coupling boolean gene and protein networks. Artificial Life, 18(2), 223-236

This article presents an abstract, tunable model containing two of the principal information-processing features of cells and explores its use with simulated evolution. The random Boolean model of genetic regulatory networks is extended to include a... Read More about A simple computational cell: Coupling boolean gene and protein networks.

Evolving boolean networks with structural dynamism (2012)
Journal Article
Bull, L. (2012). Evolving boolean networks with structural dynamism. Artificial Life, 18(4), 385-397. https://doi.org/10.1162/ARTL_a_00073

This short article presents an abstract, tunable model of genomic structural change within the cell life cycle and explores its use with simulated evolution. A well-known Boolean model of genetic regulatory networks is extended to include changes in... Read More about Evolving boolean networks with structural dynamism.

On natural genetic engineering: structural dynamism in random boolean networks (2012)
Journal Article
Bull, L. (2012). On natural genetic engineering: structural dynamism in random boolean networks

This short paper presents an abstract, tunable model of genomic structural change within the cell lifecycle and explores its use with simulated evolution. A well-known Boolean model of genetic regulatory networks is extended to include changes in nod... Read More about On natural genetic engineering: structural dynamism in random boolean networks.