Download Algorithms in a Nutshell by George T. Heineman, Stanley Selkow PDF

By George T. Heineman, Stanley Selkow

ISBN-10: 059651624X

ISBN-13: 9780596516246

Developing powerful software program calls for using effective algorithms, yet programmers seldom take into consideration them till an issue happens. Algorithms in a Nutshell describes plenty of current algorithms for fixing various difficulties, and is helping you choose and enforce the fitting set of rules on your wishes -- with simply enough math to allow you to comprehend and learn set of rules performance.

With its specialise in program, instead of idea, this booklet presents effective code suggestions in different programming languages for you to simply adapt to a particular venture. every one significant set of rules is gifted within the type of a layout development that incorporates info that will help you comprehend why and whilst the set of rules is appropriate.

With this ebook, you will:

•Solve a selected coding challenge or increase at the functionality of an current solution
•Quickly find algorithms that relate to the issues you must clear up, and be certain why a selected set of rules is the precise one to use
•Get algorithmic recommendations in C, C++, Java, and Ruby with implementation tips
•Learn the predicted functionality of an set of rules, and the stipulations it must practice at its best
•Discover the influence that related layout judgements have on diverse algorithms
•Learn complex info constructions to enhance the potency of algorithms

With Algorithms in a Nutshell, you'll methods to increase the functionality of key algorithms crucial for the good fortune of your software program purposes.

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Foundations of Genetic Algorithms. 2 . Morgan Kaufmann Publishers, San Mateo, California, p. 322 . D . (1995) . Foundations of Genetic Algorithms . 3. Morgan Kaufmann Publishers, San Francisco, California, p. 336 . H. R . (1993) . The use of neural networks for variable selection in QSAR . Bio. Med. Chem . Lett. 3, 645-651 . Xiao, Y. E. (1993) . Genetic algorithm : A new approach to the prediction of the structure of molecular clusters. Chem . Phys. Lett. 215, 17-24 . L . E. (1994) . Genetic algorithms for docking of actinomyci n D and deoxyguanosine molecules with comparison to the crystal structure of actinomycin D-deoxyguanosine complex .

1995) . Application of genetic algorithm to th e calculation of bound states and local density approximations . J. Chem . Phys. 102, 1859-1862. T. LUK E International Business Machines Corporation, 522 Sout h Road, Poughkeepsie, NY 12601, US A Genetic methods (genetic algorithms, evolutionary strategies, evolutionary programming, and their variants) represent powerful procedures that can be used to optimize many problems associated with drug design . , a population) and use them to generate new solutions .

And Marom, E . (1993) . Using the functional behavior of neuron s for genetic recombination in neural nets training . Complex Syst. 7, 445-467 . P. K . (1995) . Using a genetic algorithm to suggest combi natorial libraries . J. Chem . Inf. Comput. Sci. 35, 310-320 . M . (1993) . Crossover or mutation? In, Foundations of Genetic Algorithms . 2 . D . ) . Morgan Kaufmann Publishers, San Mateo, California, pp. 221-237 . M. A . (1991) . An analysis of multi-point crossover . E. ) . Morgan Kaufman n Publishers, San Mateo, California, pp .

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