Optimisation of process algebra models using evolutionary computation

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.