Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)

Description for Scalable Optimization via Probabilistic Modeling From Algorithms to Applications Studies in Computational Intelligence

Martin Pelikan, "Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)"
Springer; 1st edition (November 14, 2006) | English | 3540349537 | 350 pages | PDF | 5.94 MB
Machine-learning methods continue to stir the public's imagination due to its futuristic implications. But, probability-based optimization methods can have great impact now on many scientific multiscale and engineering design problems, especially true with use of efficient and competent genetic algorithms (GA) which are the basis of the present volume. Even though efficient and competent GAs outperform standard techniques and prevent negative issues, such as solution stagnation, inherent in the older but more well-known GAs, they remain less known or embraced in the scientific and engineering communities. To that end, the editors have brought together a selection of experts that (1) introduce the current methodology and lexicography of the field with illustrative discussions and highly useful references, (2) exemplify these new techniques that dramatic improve performance in provable hard problems, and (3) provide real-world applications of these techniques, such as antenna design.

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