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 aTransfer Data into Golda. New buzzwords like adata mininga, agenetic algorithmsa, and aswarm optimizationa have enriched the top executivesa vocabulary to make them look more avisionarya for the 21st century. The phrase afuzzy matha 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.Even more, very often it is the winner of the game aBullshit Bingoa popular in the corporate world.1 Fortunately, competitive advantage is beyond the hype and has a very clear business meaning and as such is at the basis of almost any economic analysis and strategic decision-making. ... Usually the focus is on academic comparative studies of the technical supremacy or limitations between methods.
|Title||:||Applying Computational Intelligence|
|Publisher||:||Springer Science & Business Media - 2009-11-28|