abstract = "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 pi-calculus, is used
to denote processes. Investigations consider variations
of GP representations for a subset of the stochastic
pi-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
notes = "GECCO-2009 A joint meeting of the eighteenth
international conference on genetic algorithms
(ICGA-2009) and the fourteenth annual genetic
programming conference (GP-2009).