S. N. Sivanandam, S. N. Deepa

Introduction to Genetic Algorithms (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 442
• Erscheinungstermin: 24. Oktober 2007
• Englisch
• ISBN-13: 9783540731900
• Artikelnr.: 37367125

Autorenporträt

S.N. Sivanandam, PSG College of Technology, Coimbatore, India / S. N. Deepa, PSG College of Technology, Coimbatore, India

Inhaltsangabe

Evolutionary Computation.- Genetic Algorithms.- Terminologies and Operators of GA.- Advanced Operators and Techniques in Genetic Algorithm.- Classification of Genetic Algorithm.- Genetic Programming.- Genetic Algorithm Optimization Problems.- Genetic Algorithm Implementation Using Matlab.- Genetic Algorithm Optimization in C/C++.- Applications of Genetic Algorithms.- to Particle Swarm Optimization and Ant Colony Optimization.