This book delivers theoretical and practical knowledge on extension of Genetic Programming (GP) for practical applications. It provides a methodology for integrating Genetic Programming and machine-learning techniques. The developmentof such tools contributes to the establishment of a more robust evolutionary framework when addressing tasks from such areas as chaotic time-series prediction, system identification, financial forecasting, classification, and data mining.
Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. Written by a leading authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source codes and GUIs are available for download from the author's website.