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Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions Key Features 1. Complete coverage on practical implementation of genetic algorithms. 2. Intuitive explanations and visualizations supply theoretical concepts. 3. Added examples and use-cases on the performance of genetic algorithms. 4. >Description Genetic algorithms are one of the most straightforward and powerful techniques used in machine learning. This book 'Learning Genetic Algorithms with Python' guides the reader right from the basics of genetic algorithms to its real practical implementation in production environments. >What you will learn 5. Understand the mechanism of genetic algorithms using popular python libraries. 6. Learn the principles and architecture of genetic algorithms. 7. Apply and Solve planning, scheduling and analytics problems in Enterprise applications. 8. >Who this book is for >Table of Contents 1. Introduction 2. Genetic Algorithm Flow 3. Selection 4. Crossover 5. Mutation 6. Effectiveness 7. Parameter Tuning 8. Black-box Function 9. Combinatorial Optimization: Binary Gene Encoding 10. Combinatorial Optimization: Ordered Gene Encoding 11. Other Common Problems 12. Adaptive Genetic Algorithm >About the Author Ivan Gridin is a mathematician, fullstack developer, data scientist, and machine learning expert living in Moscow, Russia. Over the years, he worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the key areas of his research is design and analysis of predictive time series models. Ivan has fundamental math skills in probability theory, random process theory, time series analysis, machine learning, deep learning, and optimization. He also has an in-depth knowledge and understanding of various programming languages such as Java, Python, PHP, and MATLAB. He is a loving father, husband, and collector of old math books. LinkedIn Profile: www.linkedin.com/in/survex Blog links: https: //www.facebook.com/ivan.gridin