Evolutionary Learning and Stochastic Process Algebra

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Brian J. Ross: Evolutionary Learning and Stochastic Process Algebra. 1st International Workshop on Induction of Process Models, ICML 2007, Corvallis, OR, June 2007.



This extended abstract discusses research us- ing genetic programming to evolve stochastic processes, as modelled in the stochastic pi- calculus.

Extended Abstract


Used References

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Kitagawa, J., & Iba, H. (2003). Identifying Metabolic Pathways and Gene Regulation Networks with Evo- lutionary Algorithms. In G. Fogel and D. Corne (Eds.), Evolutionary computation in bioinformatics, 255–278. Morgan Kaufmann.

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Milner, R. (1989). Communication and Concurrency. Prentice Hall.

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Ross, B. (1998a). Pairwise Sequence Comparison and the Genetic Programming of Iterative Concurrent Programs. Proc. Genetic Programming 1998 (pp. 338–343). Morgan Kaufmann.

Ross, B. (1998b). The Evolution of Concurrent Pro- grams. Applied Intelligence, 8, 21–32.

Ross, B. (2001). Logic-based Genetic Programming with Definite Clause Translation Grammars. New Generation Computing, 19, 313–337.

Ross, B. (2007). Using Genetic Programming to Syn- thesize Monotonic Stochastic Processes. Proceedings CI-2007. Banff, AB, Canada.

Speith, C., Streichert, F., Speer, N., & Zell, A. (2004). Iteratively inferring gene regulatory networks with virtual knockout experiments. Proc. 2nd European Workshop on Evolutionary Bioinformatics (pp. 102– 112). Springer. LNCS 3005.


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