The Algorithm Design Manual (2nd Edition) by Steven S. Skiena

By Steven S. Skiena

This quantity is helping take a few of the "mystery" out of selecting and working with key algorithms. Drawing seriously at the author's personal real-world reviews, the e-book stresses layout and research. insurance is split into components, the 1st being a basic consultant to thoughts for the layout and research of laptop algorithms. the second one is a reference part, which incorporates a catalog of the seventy five most vital algorithmic difficulties. via searching this catalog, readers can quick establish what the matter they've got encountered is termed, what's identified approximately it, and the way they need to continue in the event that they have to remedy it. This ebook is perfect for the operating expert who makes use of algorithms each day and has desire for a convenient reference. This paintings may also with ease be utilized in an upper-division path or as a pupil reference advisor. THE set of rules layout handbook comes with a CD-ROM that comprises: * a whole hypertext model of the total published publication. * the resource code and URLs for all brought up implementations. * over 30 hours of audio lectures at the layout and research of algorithms are supplied, all keyed to online lecture notes.

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ACM Computing Surveys (CSUR) 27(3), 326–327 (1995) 65. : On representation and genetic operators in evolutionary algorithms. Tech. Rep. CI–41/98, Fachbereich Informatik, Universit¨ at Dortmund (1998) ´ 66. : On evolutionary exploration and exploitation. Fundamenta Informaticae 35(1-4), 35–50 (1998) 40 T. Weise et al. 67. : Punctuated equilibria: an alternative to phyletic gradualism. M. ) Models in Paleobiology, ch. 5, pp. 82– 115. H. Freeman, New York (1972) 68. : Punctuated equilibria: The tempo and mode of evolution reconsidered.

849–858 (2000); KanGAL Report No. 200001 62. : A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002) 63. : Multi-objective test problems, linkages, and evolutionary methodologies. In: Genetic and Evolutionary Computation Conference, GECCO, pp. 1141–1148. ACM, New York (2006) 64. : Overfitting and undercomputing in machine learning. ACM Computing Surveys (CSUR) 27(3), 326–327 (1995) 65. : On representation and genetic operators in evolutionary algorithms.

For every problem where a given method leads to good results, we can construct a problem where the same method has exactly the opposite effect (see Section 4). As a matter of fact, doing so is even a common practice to find weaknesses of optimization algorithms and to compare them with each other. Another interpretation is that every useful optimization algorithm utilizes some form of problem-specific knowledge. Radcliffe [167] states that without such knowledge, search algorithms cannot exceed the performance of simple enumerations.

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