The synthesis of stochastic processes using genetic programming is investigated. Stochastic process behaviours take the form of time series data, in which quantities of interest vary over time in a probabilistic, and often noisy, manner. A suite of statistical feature tests are performed on time series plots from example processes, and the resulting feature values are used as targets during evolutionary search. A process algebra, the stochastic π-calculus, is used to denote processes. Investigations consider variations of GP representations for a subset of the stochastic π-calculus, for example, the use of channel unification, and various grammatical constraints. Target processes of varying complexity are studied. Results show that the use of grammatical GP with statistical feature tests can successfully synthesize stochastic processes. Success depends upon a selection of appropriate feature tests for characterizing the target behaviour, and the complexity of the target process.
REF examined the use of genetic programming to tackle this problem for a subset of the stochastic π -calculus.
Evolving stochastic processes using feature tests and genetic programming
GECCO '09
Computer Science