Description
Taylor and Francis Ltd Nature-Inspired Algorithms 1st Edition 2022 Hardbound by Kumar Misra, Krishan
This comprehensive reference text discusses nature inspired algorithms and their applications. It presents the methodology to write new algorithms with the help of MATLAB programs and instructions for better understanding of concepts. It covers well-known algorithms including evolutionary algorithms, genetic algorithm, particle Swarm optimization and differential evolution, and recent approached including gray wolf optimization. A separate chapter discusses test case generation using techniques such as particle swarm optimization, genetic algorithm, and differential evolution algorithm._x000D__x000D_The book- _x000D__x000D__x000D__x000D__x000D__x000D__x000D_Discusses in detail various nature inspired algorithms and their applications _x000D__x000D__x000D__x000D_Provides MATLAB programs for the corresponding algorithm _x000D__x000D__x000D__x000D_Presents methodology to write new algorithms _x000D__x000D__x000D__x000D_Examines well-known algorithms like the genetic algorithm, particle swarm optimization and differential evolution, and recent approaches like gray wolf optimization._x000D__x000D__x000D__x000D_Provides conceptual linking of algorithms with theoretical concepts _x000D__x000D_The text will be useful for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering. _x000D__x000D_Discussing nature inspired algorithms and their applications in a single volume, this text will be useful as a reference text for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering. It discusses important algorithms including deterministic algorithms, randomized algorithms, evolutionary algorithms, particle swarm optimization, big bang big crunch (BB-BC) algorithm, genetic algorithm and grey wolf optimization algorithm. "_x000D_ _x000D_
Preface. Acknowledgments. About the Author. Introduction. Binary Genetic Algorithms. Real-Parameter Genetic Algorithm. Differential Evolution. Particle Swarm Optimization. Grey Wolf Optimization. Environmental Adaptation Method. Other Important Optimization Algorithms. Application of Genetic Algorithms, Partial Swarm Optimization, and Differential Evolution in Software Testing. Application of Genetic Algorithms, Partial Swarm Optimization, and Differential Evolution in Regression Testing. Application of Genetic Algorithms and Partial Swarm Optimization in Cloud Computing. References and Further Reading. Index._x000D_