title: An immune network inspired evolutionary algorithm for the diagnosis of Parkinson's disease.
abstract: This paper presents a novel evolutionary algorithm inspired by protein/substrate binding exploited in enzyme genetic programming (EGP) and artificial immune networks. The immune network-inspired evolutionary algorithm has been developed in direct response to an application in clinical neurology, the diagnosis of Parkinson's disease. The inspiration for, and implementation of the algorithm is described and its performance to the application area considered.
authors.affiliation: Department of Electronics, University of York, Heslington, York, UK. sls5@ohm.york.ac.uk
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Smith
authors.forename: Jon
authors.initials: J
authors.lastname: Timmis
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D055633:Immune System Phenomena; D008962:Models, Theoretical; D010300:Parkinson Disease / Q000175:diagnosis*