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Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3226

Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Marco, David
Cairns, David
Shankland, Carron
Contact Email: dec@cs.stir.ac.uk
Title: Optimisation of Process Algebra Models Using Evolutionary Computation
Citation: Marco D, Cairns D & Shankland C (2011) Optimisation of Process Algebra Models Using Evolutionary Computation In: IEEE Congress on Evolutionary Computation (CEC), 2011, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). IEEE Congress on Evolutionary Computation, pp. 1296-1301.
Issue Date: 2011
Series/Report no.: IEEE Congress on Evolutionary Computation (CEC)
Conference Name: IEEE Congress on Evolutionary Computation
Abstract: We propose that process algebras and evolutionary algorithms have complementary strengths for developing models of complex systems. Evolutionary algorithms are powerful methods for finding solutions to optimisation problems with large search spaces but require an accurately defined fitness function to provide valid results. Process algebras are an effective method for defining models of complex interacting processes, but tuning parameters to allow model outputs to match experimental data can be difficult. Defining models in the first place can also be problematic. Our long term goal is to build a framework to synthesise process algebra models. Here we present a first step in that development: combining process algebra with an evolutionary approach to fine tune the numeric parameters of predefined models. The Evolving Process Algebra (EPA) framework is demonstrated through examples from epidemiology and computer science.
Type: Conference Paper
Status: Publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.
URI: http://hdl.handle.net/1893/3226
Affiliation: University of Stirling
Computing Science - CSM Dept
Computing Science - CSM Dept

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