Publications

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Watson, J., Maetschke, S. & Wiles, J. (2007). Dsweep: A lightweight tool for distributed parameter sweeps. In Proceedings of the Seventh International Workshop on Information Processing in Cells and Tissues, pp. 400-408.
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Watson, J. & Wiles, J. (2007). Modeling the fitness of plant morphologies across three levels of complexity. In Proceedings of the Seventh International Workshop on Information Processing in Cells and Tissues, pp. 376-386.
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Watson, J. (2006). From genotypes to phenotypes and back again: modeling the interaction between individual behaviour and evolution. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland.
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Wiles, J., Watson, J., Tonkes, B. & Deacon, T.W. (2005). Transient phenomena in learning and evolution: genetic assimilation and genetic redistribution. Artificial Life, Vol. 11, Issues 1-2, pp. 177-188.
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Wiles, J., Watson, J. (2005). Patterns in complex systems modeling. Lecture Notes in Computer Science, Vol. 3578 (June), pp. 532-539.
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Watson, J., Hawkins, J., Bradley, D., Dassanayake, D., Wiles, J. & Hanan, J. (2005). Towards a network pattern language for complex systems. In Abbass, H.A., Bossamaier, T., & Wiles, J. (Eds), Recent Advances in Artificial Life, Vol. 3 of Advances in Natural Computation, pp. 309-317,349-358. World Scientific.
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Watson, J., Geard, N. & Wiles, J. (2004). Towards more biological mutation operators in gene regulation studies. BioSystems, Vol. 76, Issues 1-3, pp. 239-248.
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Watson, J., Abbass, H.A., Lokan, C. & Lindsay, P. (2004). Software engineering for artificial life, complex systems, and agent-based distillation. In Proceedings of the 7th Asia-Pacific Complex Systems Conference (Complex2004), pp. 649-661.
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Watson, J., Wiles, J. & Hanan, J. (2003). Towards more relevant evolutionary models: Integrating an artificial genome with a developmental phenotype. In proceedings of The Australian Conference on Artificial Life (ACAL 2003), pp. 288-298.
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Watson, J., Geard, N. & Wiles, J. (2002). Stability and task complexity: A neural network model of evolution and learning. In R.K. Standish, M.A. Bedau & H.A. Abbass (Eds), Proceedings of the 8th International Conference on Artificial Life (Artificial Life VIII), pp. 153-156. MIT Press.
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Watson, J. & Wiles, J. (2002). The rise and fall of learning: A neural network model of the genetic assimilation of acquired traits. In Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 600-605.
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Wiles, J., Tonkes, B. & Watson, J. (2001). How learning can guide evolution in hierarchical modular tasks. In J. D. Moore & K. Stenning (Eds), Proceedings of the 23rd Annual Conference of the Cognitive Science Society (CogSci 2001), pp. 1130-1135. Lawrence Erlbaum Associates.
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