Using genetic programming to synthesize monotonic stochastic processes

Authors:
Brian J. Ross
Affiliations:
Brock University, St. Catharines, Ontario, Canada
Venue:
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Year:
2007

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Abstract

The automatic synthesis of stochastic concurrent processes is investigated. We use genetic programming to automatically evolve a set of stochasdtic π-calculus expressions that generate execution behaviour conforming to some supplied target behaviour. We model the stochastic π-calculus in a grammatically-guided genetic programming system, and we use an efficient interpreter based on the SPIM abstract machine model by Phillips and Cardelli. The behaviours of target systems are modelled as streams of numerical time series for different variables of interest. We were able to successfully evolve stochastic π-calculus systems that exhibited the target behaviors. Successful experiments considered target processes with continuous monotonic behaviours.