tailieunhanh - Báo cáo hóa học: " Department of General Engineering, University of Illinois"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Department of General Engineering, University of Illinois | EURASIP Journal on Applied Signal Processing 2003 8 731-732 2003 Hindawi Publishing Corporation Foreword David E. Goldberg Department of General Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Email deg@ I was delighted when I was asked to write a foreword to this special issue on genetic algorithms GAs and evolutionary computation EC in image and signal processing edited by Riccardo Poli and Stefano Cagnoni for two reasons. First the special issue is another piece of the mounting evidence that GAs and EC are finding an important niche in the solution of difficult real-world problems. Second in reviewing the contents of the special issue I find it almost archetypal in its reflection of the GA EC applications world of 2003. In the remainder of this discussion I briefly review a number of reasons why genetic and evolutionary techniques are becoming more and more important in real problems and discuss some of the ways this issue used to both demonstrate effective GA EC application and foreshadow more signal and image processing by evolutionary and genetic means. There are a number of reasons why GAs and EC are becoming more prevalent in real applications. The first reason is what I call the buzz. Let us face it GAs are cool. The very idea of doing a Darwinian survival of the fittest and genetics on a computer is neat. But cool and neat while they may attract our attention do not merit our sustained involvement. Another reason for which GAs have become more popular is the motivation from artificial systems. Although decades even centuries of optimization and operations research leave us with an impressive toolkit the contingency basis of the methodology leaves us somewhat cold. By this I mean that the selection of an optimization technique or OR is contingent on the type of problem you face. If you have a linear problem with linear constraints you choose linear programming. If you have a stage decomposable problem you choose dynamic .

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