Spectral analysis via supervised genetic search with application-specific mutations
J. Taylor, J.J. Rowland, Douglas Bruce Kell · 2002
We present a method in which a genetic algorithm is used to optimise an expression in order to provide a supervised method for interpretation of the infrared analytical spectra of complex biological samples. The aim is to produce a model that can predict the value of a measurand of interest, such as the concentration of a particular chemical constituent, from a complex infrared spectrum of biological material. The method we describe is in some ways analogous to genetic programming but it more readily allows the output expression to be constrained in complexity and permits its general form to be specified by the user, thereby enhancing its explanatory ability. The quasi-continuous properties of optical spectra are exploited by mutations that explore spectral regions adjacent to selected variables, and provide adaptive averaging of spectral regions so as to provide selective optimisation of the tradeoff between spectral resolution and signal-to-noise ratio.