Hands-On Genetic Algorithms with Python
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Section 1: The Basics of Genetic Algorithms
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An Introduction to Genetic Algorithms
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Understanding the Key Components of Genetic Algorithms
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Section 2: Solving Problems with Genetic Algorithms
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Using the DEAP Framework
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Combinatorial Optimization
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Constraint Satisfaction
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Optimizing Continuous Functions
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Section 3: Artificial Intelligence Applications of Genetic Algorithms
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Enhancing Machine Learning Models Using Feature Selection
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Hyperparameter Tuning of Machine Learning Models
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Architecture Optimization of Deep Learning Networks
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Reinforcement Learning with Genetic Algorithms
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Section 4: Related Technologies
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Genetic Image Reconstruction
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Other Evolutionary and Bio-Inspired Computation Techniques
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About this book
Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence.
After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications.
By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.
- Publication date:
- January 2020
- Publisher
- Packt
- Pages
- 346
- ISBN
- 9781838557744