Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Information Processing Letters
Logic-based genetic programming with definite clause translation grammars
New Generation Computing
Communication and Concurrency
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature-based classification of time-series data
Information processing and technology
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Evolution of mathematical models of chaotic systems based on multiobjective genetic programming
Knowledge and Information Systems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Using feature-based fitness evaluation in symbolic regression with added noise
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Using genetic programming to synthesize monotonic stochastic processes
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Evolving noisy oscillatory dynamics in genetic regulatory networks
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
A compositional approach to the stochastic dynamics of gene networks
Transactions on Computational Systems Biology IV
The evolution of higher-level biochemical reaction models
Genetic Programming and Evolvable Machines
Evolving Bio-PEPA process algebra models using genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
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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.