Evolving Coupled Map Lattices for Computation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Diagnosis of Parkinson's disease using evolutionary algorithms
Genetic Programming and Evolvable Machines
Controlling complex dynamics with artificial biochemical networks
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Natural Computing: an international journal
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Artificial biochemical networks (ABNs) are a class of computational automata whose architectures are motivated by the organisation of genetic and metabolic networks. In this work, we investigate whether evolved ABNs can carry out classification when stimulated with time series data collected from human subjects with and without Parkinson's disease. The evolved ABNs have accuracies in the region of 80-90%, significantly higher than the diagnostic accuracies typically found in initial clinical diagnosis. We also show that relatively simple ABNs, comprising only a small number of discrete maps, are able to recognise the abnormal patterns of motor function associated with Parkinson's disease.