Nature-inspired algorithms : for engineers and scientists
著者
書誌事項
Nature-inspired algorithms : for engineers and scientists
CRC Press, [2023]
First edition
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注記
Content Type: text (rdacontent), Media Type: unmediated (rdamedia), Carrier Type: volume (rdacarrier)
Includes bibliographical references and index
Summary: "The text discusses nature inspired algorithms and their applications in a comprehensive manner. It will be an ideal reference text for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering"-- Provided by publisher
内容説明・目次
内容説明
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.
The book-
Discusses in detail various nature inspired algorithms and their applications
Provides MATLAB programs for the corresponding algorithm
Presents methodology to write new algorithms
Examines well-known algorithms like the genetic algorithm, particle swarm optimization and differential evolution, and recent approaches like gray wolf optimization.
Provides conceptual linking of algorithms with theoretical concepts
The text will be useful for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering.
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. "
目次
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.
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