Introduction to Genetic Algorithms

Sivanandam, S.N. (PSG College of Technology); Deepa, S.N.

ISBN 10: 354073189X / ISBN 13: 9783540731894

Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2007

Riguardo questo articolo

Offers a basic introduction to genetic algorithms. This book provides an explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. It presents implementation of optimization problems using C and C++ as well as solutions for genetic algorithm problems using MATLAB 7.0. Num Pages: 442 pages, 1, black & white illustrations. BIC Classification: UGK; UYQ. Category: (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 245 x 162 x 29. Weight in Grams: 844. . 2007. Hardback. . . . .

Codice articolo V9783540731894

Dati bibliografici

• Titolo: Introduction to Genetic Algorithms
• Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 354073189X
• ISBN 13: 9783540731894
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.

Dalla quarta di copertina

Genetic Algorithms are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of Genetic Algorithms is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. This book is designed to provide an in-depth knowledge on the basic operational features and characteristics of Genetic Algorithms. The various operators and techniques given in the book are pertinent to carry out Genetic Algorithm Research Projects. The book also explores the different types are Genetic Algorithms available with their importance. Implementation of Genetic Algorithm concept has been performed using the universal language C/C++ and the discussion also extends to Genetic Algorithm MATLAB Toolbox. Few Genetic Algorithm problems are programmed using MATLAB and the simulated results are given for the ready reference of the reader. The applications of Genetic Algorithms in Machine learning, Mechanical Engineering, Electrical Engineering, Civil Engineering, Data Mining, Image Processing, and VLSI are dealt to make the readers understand where the concept can be applied.