Dr Tom Osborn

Adjunct Professor, School of Software
 
Can supervise: Yes

Conference Papers

Kennedy, P.J. & Osborn, T.R. 2001, 'A double-stranded Encoding Scheme with inversion operator for Genetic Algorithms', Genetic and Evolutionary Computation Conference, San Francisco, USA, July 2001 in Proceedings of Genetic and Evolutionary Computation Conference, ed Spector L, Goodman E, Wu A, Langdon W, Voight HM, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M, Burke E, Morgan Kaufmann, San Francisco, pp. 398-407.
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Kennedy, P.J. & Osborn, T.R. 2000, 'Evolution of Adaptive Behaviour in a Simulated Single-Celled Organism', Paris, France, September 2000 in SAB 2000 Proceedings Supplement Book; Sixth International Conference on Simulation of Adaptive Behaviour: From Animals to Animats, ed Meyu, Berthot, Floreano, Roitblat and Wilson, The International Society for Adaptive Behavior, Honolulu, USA, pp. 225-234.
Kennedy, P.J. & Osborn, T.R. 2000, 'Operon Expression and Regulation with Spiders', Las Vegas, Nevada, USA, July 2000 in Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, ed Wu, A.S., N/A, N/A, pp. a161-a166.

Journal Articles

Kennedy, P.J. & Osborn, T.R. 2001, 'A Model of Gene Expression and Regulation in an Artificial Cellular Organism', Complex Systems, vol. 13, no. 1.
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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.