Genetic algorithms for spectral pattern recognition

Abstract

The development of a genetic algorithm (GA) for pattern recognition analysis of spectral data is reported. The GA identifies a set of features (wavelengths) that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features is about differences between classes in the dataset. In addition, the GA focuses on those classes and or samples that are difficult to classify as it trains using a form of boosting to modify the fitness landscape. Boosting minimizes the problem of convergence to a local optimum since the fitness function of the GA is changing as the population is evolving towards a solution. Over time, samples that consistently classify correctly are not as heavily weighted in the analysis as samples that are difficult to classify. The pattern recognition GA learns its optimal parameters in a manner similar to a neural network. The algorithm integrates aspects of both strong and weak learning to yield a “smart” one-pass procedure.