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Dr Tom Osborn

Adjunct Professor, School of Software
 
Can supervise: Yes

Conferences

Kennedy, P.J. & Osborn, T. 2001, 'A double-stranded Encoding Scheme with inversion operator for Genetic Algorithms', Proceedings of Genetic and Evolutionary Computation Conference, Morgan Kaufmann, San Francisco, pp. 398-407.
Kennedy, P.J. & Osborn, T. 2000, 'Evolution of Adaptive Behaviour in a Simulated Single-Celled Organism', SAB 2000 Proceedings Supplement Book; Sixth International Conference on Simulation of Adaptive Behaviour: From Animals to Animats, The International Society for Adaptive Behavior, Honolulu, USA, pp. 225-234.
Willey, K., Osborn, T., Eckert, M.P., Liwanag, R. & Reisenfeld, S. 1999, 'The Suitability of Using NORAD TLE's to Track LEO Satellites with Ka Band Communications', Proceedings 5th Ka Band Utilization Conference, Instituto Internazionale Delle Comunicazioni, Italy.

Journal articles

Pullen, A. & Osborn, T. 2002, 'What do You Want from Me? A Poststructuralist Feminist Reading of Middle Managers' Identities', Culture and Organization, vol. 8, no. 1, pp. 1-21.
One of the first papers in the field to apply poststructural theory to management identities
Kennedy, P.J. & Osborn, T. 2001, 'A Model of Gene Expression and Regulation in an Artificial Cellular Organism', Complex Systems, vol. 13, no. 1.
Gene expression and regulation may be viewed as a parallel parsing algorithm---translation from a genomic language to a phenotype. We describe a model of gene expression and regulation based on the operon model of Jacob and Monod. Operons are groups of genes regulated in the same way. An artificial cellular metabolism expresses operons encoded on a genome in a parallel genomic language. This is accomplished using an abstract entity called a spider. A genetic algorithm is used to evolve the simulated cells to adapt to a simple environment. Genomes are subjected to recombination, mutation, and inversion operators. Observations from this experiment suggest four areas to explore: dynamic environments for the evolution of regulation, advantages of time lags inherent in the expression algorithm, sensitivity of our genomic language, and noncoding regions on the genome. Issues relating to the application of the expression model to evolutionary computation are discussed.