Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A compositional approach to performance modelling
A compositional approach to performance modelling
A brief history of process algebra
Theoretical Computer Science - Process algebra
The crucial role of CS in systems and synthetic biology
Communications of the ACM - Web searching in a multilingual world
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Evolving stochastic processes using feature tests and genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Bio-PEPA: A framework for the modelling and analysis of biological systems
Theoretical Computer Science
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
Design and development of software tools for Bio-PEPA
Winter Simulation Conference
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
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This paper presents initial results of applying a Genetic Programming (GP) approach to the evolution of process algebra models defined in Bio-PEPA. An incomplete model of a system is provided together with target behaviour. GP is then used to evolve new definitions that complete the model while ensuring a good fit to target data. Our results show that a set of effective models can be developed with this approach that can either be used directly or further refined using a modeller's domain knowledge. Such an approach can greatly reduce the time taken to develop new models, enabling a modeller to focus on the subtler modelling aspects of the problem domain. Although the work presented here concerns the modelling of biological systems, the approach is generally applicable to systems for which appropriate target behaviour can be captured and that can be formalised as a set of communicating processes.