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- Published Year: 2007
- Page count: 453
- File Size: 9 MB
- Language: English
- Published by: Springer
- Visited by: 384
- Rating/Review: 3.8
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ISBN:
354073189X
9783540731894
Keywords:
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Introduction to Genetic Algorithms
3.8
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Persian Summary
Welcome to an enriching exploration of evolutionary computation through the lens of one of the most fascinating topics in the field of artificial intelligence – Genetic Algorithms. Our book, 'Introduction to Genetic Algorithms', authored by S.N. Sivanandam and S. N. Deepa, serves as a comprehensive guide for understanding the intricate working of genetic algorithms. With an affinity for simplicity and clarity, this book is tailored to meet the needs of both beginners and advanced learners.
Detailed Summary of the Book
The book delves into the fundamentals of genetic algorithms (GAs), a class of optimization techniques inspired by the principles of natural selection and genetics. We commence with an introductory chapter that lays down the basic concepts, illustrating how genetic algorithms emulate biological evolution. Progressing through the chapters, you will discover a meticulous breakdown of the genetic algorithm's structure, comprising selection, crossover, and mutation processes, each elucidated with mathematical rigor and real-world examples.
Subsequent chapters explore the application of genetic algorithms in various fields such as engineering, economics, and computer science, demonstrating their versatility and efficacy in solving complex optimization problems. You'll gain insights into decoding strategies, constraint handling, and multi-objective optimization, supported by case studies and practical scenarios. Special discussions on hybrid approaches that combine GAs with other computational techniques provide readers with a nuanced understanding of advanced methodologies.
Key Takeaways
By the end of this book, readers will:
- Understand the core principles of genetic algorithms and their operational mechanisms.
- Acquire proficiency in formulating and implementing genetic algorithms for complex problem-solving.
- Learn to apply genetic algorithms in diverse fields to enhance computational efficiency and optimize performance.
- Grasp the advanced concepts of hybrid systems and multi-objective optimization for tackling intricate challenges.
Famous Quotes from the Book
To inspire and encapsulate the essence of genetic algorithms, we've included definitive quotes from the book that resonate with our readers:
"Embrace the chaos of evolution as a creative force for innovation."
"In the symphony of computation, genetic algorithms compose the melodies of nature-inspired optimization."
Why This Book Matters
This book stands as a pivotal resource in the AI and machine learning community, bridging theoretical concepts with practical applications. Its importance is multifaceted:
- Educational Value: The methodical approach caters to both academic pursuits and self-learners, providing a foundational yet thorough understanding of genetic algorithms.
- Cross-Disciplinary Relevance: As genetic algorithms find applications across various fields, this book equips readers with the tools to explore and innovate in their specific domains.
- Problem-Solving Skills: Readers are encouraged to think critically and develop robust problem-solving skills, essential for tackling real-world challenges in technology and research.
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