In this book, it is shown how pattern recognition approaches developed in computer science can be seamlessly combined with statistical techniques to produce a knowledge driven search algorithm. Two different classes of applications are used to demonstrate the wide applicability of the algorithm. The first class of applications is in global design optimization. In design optimization applications, Bayesian classification tree is used to generate high-performing design alternatives. The second is in the structural mechanics of systems with spatially random properties. In applications involving spatially random properties, the classification tree provides pattern-based features that can be used to identify regions of a system with desirable or undesirable local properties. The theories and example applications should be especially useful to researchers and professionals in the field of uncertainty modeling and design optimization.

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