Titlebook: Biologically-Inspired Optimisation Methods; Parallel Algorithms, Andrew Lewis,Sanaz Mostaghim,Marcus Randall Book 2009 Springer-Verlag Berl

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Andrew Lewis,Sanaz Mostaghim,Marcus RandallPresents recent research in Biologically-inspired Optimisation Methods

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Studies in Computational Intelligencehttp://image.papertrans.cn/b/image/187533.jpg

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Robin Willems,Dirk Lecluse,J. P. Kruthying efficient methodologies based on nature-inspired computational algorithms for optimization. In this chapter, we briefly describe the original motivation for developing EMO algorithms and provide an account of some successful problem domains on which EMO has demonstrated a clear edge over their

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F. Mandorli,U. Cugini,H. E. Otto,F. Kimurauted platform and is in daily use for searching a database of molecules. The core of the approach uses an ‘inverted’ relaxation labelling method that finds a good match of the input data with stored examples. The method is shown to scale linearly with the number of graphs, and to scale linearly unde

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