Written by leaders in this field, Genetic Programming and Machine Learning Applied delineates the extension of Genetic Programming (GP) for practical applications. Reflecting the rapidly developing concepts and emerging paradigms, this book describes how to use machine learning techniques, making the learning operators that efficiently sample the search space, tracing the search process through the design of objective fitness function, and examine the performance of search systems evolution. This provides a methodology to integrate GP and machine learning techniques, build a strong evolutionary framework to handle the duties of such fields as chaotic time-series prediction, system identification, financial forecasting, classification, and data mining.