Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence
By : Dan Simon
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Material type: 
TextEdition: 1st.ISBN: 9780470937419.Subject(s): Evolutionary computation | Computer algorithms | Biologically-inspired computing | MATHEMATICS / Discrete MathematicsDDC classification: 006.3 Online resources: Click here to access online Summary: "This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. Covering the theory, history, mathematics, and applications of evolutionary optimization algorithms, this timely and practical book offers lengthy examples, a companion website, MATLAB code, and a Solutions Manual--making it perfect for advanced undergraduates, graduates, and practicing engineers involved in engineering and computer science"--
Summary: "Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear (but theoretically rigorous) understanding of Evolutionary Algorithms, with an emphasis on implementation rather than models"--
| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
e-Book
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Library of SLTC | e-Books Collection | 006.3 SIM (Browse shelf) | Not for loan |
"This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. Covering the theory, history, mathematics, and applications of evolutionary optimization algorithms, this timely and practical book offers lengthy examples, a companion website, MATLAB code, and a Solutions Manual--making it perfect for advanced undergraduates, graduates, and practicing engineers involved in engineering and computer science"--
Summary: "Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear (but theoretically rigorous) understanding of Evolutionary Algorithms, with an emphasis on implementation rather than models"--
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