Abstract
A genetic algorithm (GA) for pattern recognition analysis of optical sensor data has been developed. The GA selects feature sets based on their principal component (PC) plots. A good PC plot can only be generated using features whose variance or information is primarily about class differences. Hence, the principal component analysis routine in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it limits its search to features whose PC plots show clustering on the basis of the class. In addition, the GA focuses on those classes and/or samples that are difficult to classify as it trains using a form of boosting. Samples that consistently classify correctly are not as heavily weighted as samples that are difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a perceptron. The pattern recognition GA integrates aspects of strong and weak learning to yield a 'smart' one-pass procedure for feature selection and pattern classification.
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