Self-adaptive constructivism in neural xcs and xcsf
(2008)
Presentation / Conference
Howard, D., Bull, L., & Lanzi, P. L. (2008, June). Self-adaptive constructivism in neural xcs and xcsf. Paper presented at 2008 Genetic and Evolutionary Computation Conference
Outputs (262)
Towards designing collision based chemical logic gates with adaptive computing (2008)
Presentation / Conference
Toth, R., Stone, C., de Lacy Costello, B., Adamatzky, A., & Bull, L. (2008, June). Towards designing collision based chemical logic gates with adaptive computing. Paper presented at IPCC 2008, Concordia University, Montreal, Canada
Evolving localizations in reaction-diffusion cellular automata (2008)
Journal Article
Adamatzky, A., Bull, L., Collet, P., & Sapin, E. (2008). Evolving localizations in reaction-diffusion cellular automata. International Journal of Modern Physics C, 19(04), 557-567. https://doi.org/10.1142/S0129183108012376We consider hexagonal cellular automata with immediate cell neighbourhood and three cell-states. Every cell calculates its next state depending on the integral representation of states in its neighbourhood, i.e., how many neighbours are in each one s... Read More about Evolving localizations in reaction-diffusion cellular automata.
Partnering strategies for fitness evaluation in a pyramidal evolutionary algorithm (2008)
Journal Article
Aickelin, U., & Bull, L. (2008). Partnering strategies for fitness evaluation in a pyramidal evolutionary algorithm
Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system (2008)
Journal Article
Bull, L., Budd, A., Stone, C., Uroukov, I., Costello, B. D. L., & Adamatzky, A. (2008). Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system. Artificial Life, 14(2), 203-222. https://doi.org/10.1162/artl.2008.14.2.203We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this... Read More about Towards unconventional computing through simulated evolution: Control of nonlinear media by a learning classifier system.
Random number generation by cellular automata with memory (2008)
Journal Article
Alonso-Sanz, R., & Bull, L. (2008). Random number generation by cellular automata with memory. International Journal of Modern Physics C, 19(02), 351-367. https://doi.org/10.1142/S012918310801211XThis paper considers an extension to the standard framework of cellular automata which implements memory capabilities by featuring cells by elementary rules of its last three states. A study is made of the potential value of elementary cellular autom... Read More about Random number generation by cellular automata with memory.
Clusters and switchers in globally coupled photochemical oscillators (2008)
Journal Article
Taylor, A., Kapetanopoulos, P., Whitaker, B., Toth, R., Bull, L., & Tinsley, M. (2008). Clusters and switchers in globally coupled photochemical oscillators. Physical Review Letters, 100(21), 214101
Spherical conditions with XCSc (2008)
Report
Tamee, K., Bull, L., & Pinngern, O. (2008). Spherical conditions with XCSc
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
Initial results from the use of evolutionary learning to control chemical computers (2008)
Journal Article
Budd, A., Stone, C., Masere, J., Adamatzky, A., de Lacy Costello, B., & Bull, L. (2008). Initial results from the use of evolutionary learning to control chemical computers. International Journal of Unconventional Computing, 4(1), 13-22The behaviour of pulses of Belousov-Zhabotinski (BZ) reaction diffusion waves can be controlled automatically through machine learning. By extension, a form of chemical network computing, i.e., a massively parallel non-linear Computer, can be realise... Read More about Initial results from the use of evolutionary learning to control chemical computers.