In theory, there is no difference between theory and practice. But, in practice, there is. Jan L. A. van de Snepscheut The ?ow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thousands of applications have proved the practical potential of fuzzy logic, neural networks, evolutionary com- tation, swarm intelligence, and intelligent agents even before their theoretical foundation is completely understood. And the popularity is rising. Some software vendors have pronounced the new machine learning gold rush to "Transfer Data into Gold". New buzzwords like "data mining", "genetic algorithms", and "swarm optimization" have enriched the top executives' vocabulary to make them look more "visionary" for the 21st century. The phrase "fuzzy math" became political jargon after being used by US President George W. Bush in one of the election debates in the campaign in 2000. Even process operators are discussing the perf- mance of neural networks with the same passion as the performance of the Dallas Cowboys. However, for most of the engineers and scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more dif?cult task.

Arthur K. Kordon is a Data Mining and Modeling Leader in the Data Mining and Modeling Capability of The Dow Chemical Company. He is an internationally recognized expert in applying emerging technologies in industry, and has given talks and chaired panels on the topic at the major computational intelligence conferences such as WCCI and GECCO. He has successfully introduced several novel technologies for improved manufacturing and new product design in the chemical industry, and his research interests include application issues of computational intelligence, robust empirical modeling, intelligent process monitoring and control, and data mining.

Part I: Computational Intelligence for the Masses: Computational vs. Artificial Intelligence.- A Roadmap Through the Computational Intelligence Maze.- Fuzzy Systems: Let¿s Get Fuzzy.- Machine Learning: The Ghost in the Learning Machine.- Evolutionary Computation: The Profitable Gene.- Swarm Intelligence: The Benefits of the Swarms.- Intelligent Agents: The Computer Intelligence Agency (CIA).- Part II: Computational Intelligence Creates Value: Why We Need Intelligent Solutions?.- Competitive Advantages of Computational Intelligence.- Issues in Applying Computational Intelligence.- Part III: Computational Intelligence Application Strategy: Integrate & Conquer.- How to Apply Computational Intelligence?.- Computational Intelligence Marketing.- Examples of Computational Intelligence Industrial Applications.- Part IV: The Future of Applied Computational Intelligence: Future Directions of Applied Computational Intelligence

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