Information Processing with Evolutionary Algorithms
Springer | ISBN: 1852338660 | 2004. | 339 p. | RARed | PDF | 3.6MB
The last decade of the twentieth century has witnessed a surge of interest in numerical, computational intensive approaches to information processing. The lines that draw the boundaries between statistics, optimization, artificial intelligence and information processing are disappearing and it is not uncommon to find well founded and sophisticated mathematical approaches in applications traditionally associated with ad-hoc programming. Evolutionary Algorithms are increasingly being applied to information processing applications that require any kind of optimization and they have reached the status of problem-solving tools in the backpack of the engineer…
The last decade of the 20th century has witnessed a surge of interest in numerical, computation-intensive approaches to information processing. The lines that draw the boundaries among statistics, optimization, artifical intelligence and information processing are disappearing, and it is not uncommon to find well-founded and sophisticated mathematical approaches in application domains traditionally associated with ad-hoc programming. Heuristics has become a branch of optimization and statistics. Clustering is applied to analyze soft data and to provide fast indexing in the World Wide Web. Non-trivial matrix algebra is at the heart of the last advances in computer vision.
The breakthrough impulse was, apparently, due to the rise of the interest in artifical neural networks, after its rediscovery in the late 1980s. Disguised as ANN, numerical and statistical methods made an appearance in the information processing scene, and others followed. A key component in many intelligent computational processing is the search for an optimal value of some function. Sometimes, this function is not evident and it must be made explicit in order to formulate the problem as an optimization problem. The search often takes place in high-dimensional spaces that can be either discrete, or continuous or mixed. The shape of the high-dimensional surface that corresponds to the optimized function is usually very complex. Evolutionary algorithms are increasingly being applied to information processing applications that require any kind of optimization. They provide a systematic and intuitive framework to state the optimization problems, and an already well-established body of theory that endorses their good mathematical properties. Evolutionary algorithms have reached the status of problem-solving tools in the backpack of the engineer. However, there are still exciting new developments taking place in the academic community. The driving idea in the organization of this compilation is the emphasis in the contrast between already accepted engineering practice and ongoing explorations in the academic community.
NO mirrors, please !
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