A Guide to Experimental Algorithmics

A Guide to Experimental Algorithmics
Author: Catherine C. McGeoch
Publisher: Cambridge University Press
Total Pages: 273
Release: 2012-01-30
Genre: Computers
ISBN: 1107001730


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This is a guidebook for those who want to use computational experiments to support their work in algorithm design and analysis. Numerous case studies and examples show how to apply these concepts. All the necessary concepts in computer architecture and data analysis are covered so that the book can be used by anyone who has taken a course or two in data structures and algorithms.


A Guide to Experimental Algorithmics
Language: en
Pages: 273
Authors: Catherine C. McGeoch
Categories: Computers
Type: BOOK - Published: 2012-01-30 - Publisher: Cambridge University Press

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This is a guidebook for those who want to use computational experiments to support their work in algorithm design and analysis. Numerous case studies and exampl
A Guide to Experimental Algorithmics
Language: en
Pages: 272
Authors: Catherine C. McGeoch
Categories: COMPUTERS
Type: BOOK - Published: 2012 - Publisher:

GET EBOOK

This guidebook is for those who want to use computational experiments to support their work in algorithm design and analysis.
Experimental Algorithms
Language: en
Pages: 466
Authors: Joachim Gudmundsson
Categories: Computers
Type: BOOK - Published: 2014-06-09 - Publisher: Springer

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This book constitutes the refereed proceedings of the 13th International Symposium on Experimental Algorithms, SEA 2014, held in Copenhagen, Denmark, in June/Ju
Experimental Algorithmics
Language: en
Pages: 295
Authors: Rudolf Fleischer
Categories: Computers
Type: BOOK - Published: 2003-07-01 - Publisher: Springer

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Experimental algorithmics, as its name indicates, combines algorithmic work and experimentation: algorithms are not just designed, but also implemented and test
Multimodal Optimization by Means of Evolutionary Algorithms
Language: en
Pages: 206
Authors: Mike Preuss
Categories: Computers
Type: BOOK - Published: 2015-11-27 - Publisher: Springer

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This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary al