Overview
- Editors:
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Godfrey C. Onwubolu
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Richmond Hill, Canada
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Donald Davendra
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Department of Applied Informatics, Tomas Bata Univerzity in Zlin, Zlin, Czech Republic
- Presents a complete introduction to differential evolution
- Includes the continuous space DE formulation and the permutative-based combinatorial DE formulation
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Table of contents (7 chapters)
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Front Matter
Pages I-XVII
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- Godfrey Onwubolu, Donald Davendra
Pages 1-11
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- Godfrey Onwubolu, Donald Davendra
Pages 13-34
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- Donald Davendra, Godfrey Onwubolu
Pages 35-80
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- Fatih Tasgetiren, Angela Chen, Gunes Gencyilmaz, Said Gattoufi
Pages 121-138
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- Fatih Tasgetiren, Yun-Chia Liang, Quan-Ke Pan, Ponnuthurai Suganthan
Pages 139-162
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Back Matter
Pages 207-213
About this book
What is combinatorial optimization? Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both ?nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problem’s feasible solutions - permutations of city labels - c- prise a ?nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de?ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with ?oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi?ed as combinatorial because their feasible sets are discrete. For example, the knapsack problem’s goal is to pack objects of differing weight and value so that the knapsack’s total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies ?oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer.
Editors and Affiliations
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Richmond Hill, Canada
Godfrey C. Onwubolu
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Department of Applied Informatics, Tomas Bata Univerzity in Zlin, Zlin, Czech Republic
Donald Davendra