Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorithm
Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions
- Complete coverage on practical implementation of genetic algorithms.
- Intuitive explanations and visualizations supply theoretical concepts.
- Added examples and use-cases on the performance of genetic algorithms.
- Use of Python libraries and a niche coverage on the performance optimization of genetic algorithms.
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.
Each of the chapters gives the reader an intuitive understanding of each concept. You will learn how to build a genetic algorithm from scratch and implement it in real-life problems. Covered with practical illustrated examples, you will learn to design and choose the best model architecture for the particular tasks. Cutting edge examples like radar and football manager problem statements, you will learn to solve high-dimensional big data challenges with ways of optimizing genetic algorithms.
What you will learn
- Understand the mechanism of genetic algorithms using popular python libraries.
- Learn the principles and architecture of genetic algorithms.
- Apply and Solve planning, scheduling and analytics problems in Enterprise applications.
- Expert learning on prime concepts like Selection, Mutation and Crossover.
Who this book is for
The book is for Data Science team, Analytics team, AI Engineers, ML Professionals who want to integrate genetic algorithms to refuel their ML and AI applications. No special expertise about machine learning is required although a basic knowledge of Python is expected.
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/
Cover Page Title Page Copyright Page Dedication Page About the Author About the Reviewer Acknowledgement Preface Errata Table of Contents 1. Introduction Structure 1.1 Nature of genetic algorithm 1.2 Applicability of genetic algorithms 1.3 Pros and cons of genetic algorithms 1.4 Your first genetic algorithm Conclusion Questions 2. Genetic Algorithm Flow Structure 2.1 Individual 2.2 Fitness function 2.3 Population 2.4 Selection 2.5 Crossover 2.6 Mutation 2.7 Genetic algorithm flow Conclusion Points to remember: Multiple choice questions: Answers Questions Key terms 3. Selection Structure Objectives 3.1 Tournament selection 3.2 Proportional selection 3.3 Stochastic universal sampling selection 3.4 Rank selection 3.5 Elite selection Conclusion Points to remember Multiple choice questions Answers Key terms 4. Crossover Structure Objectives 4.1 One-point crossover 4.2 N-point crossover 4.3 Uniform crossover 4.4 Linear combination crossover 4.5 Blend crossover 4.6 Order crossover 4.7 Fitness driven crossover Conclusion Points to remember Multiple choice questions Answers Questions Key terms 5. Mutation Structure Objectives 5.1 Random deviation mutation Random deviation mutation 5.2 Exchange mutation 5.3 Shift mutation 5.4 Bit flip mutation 5.5 Inversion mutation 5.6 Shuffle mutation 5.7 Fitness driven mutation Conclusion Points to remember Multiple choice questions Answers Questions Key terms 6. Effectiveness Structure Objectives 6.1 Best individual 6.2 Total number of individuals 6.3 Genetic algorithm as random variable 6.4 Monte-Carlo simulation Conclusion Points to remember Multiple choice questions Answers Key terms 7. Parameter Tuning Structure Objectives 7.1 Population size 7.2 Crossover probability 7.3 Mutation probability Conclusion Points to remember Multiple choice questions Answers Questions Key terms 8. Black-Box Function Structure Objectives 8.1 What is Black-box function? 8.2 Gene encodings 8.3 Genetic algorithm architecture Conclusion Points to remember Multiple choice questions Answers Questions Key terms 9. Combinatorial Optimization – Binary Gene Encoding Structure Objectives 9.1 Knapsack problem 9.2 Schedule problem 9.3 Radar placement problem Conclusion Questions 10. Combinatorial Optimization – Ordered Gene Encoding Structure Objectives 10.1 Travelling Salesman Problem 10.2 Football manager problem Conclusion Questions 11. Other Common Problems Structure Objective 11.1 System of equations 11.2 Graph coloring problem Conclusion Questions 12. Adaptive Genetic Algorithm Structure Objectives 12.1 Evolutionary improvement rate 12.2 Evolutionary progress and population size 12.3 Evolutionary progress, crossover, and mutation probabilities 12.4 Evolutionary dead-end and premature termination of the genetic algorithm 12.5 Example of adaptive genetic algorithm 12.6 Adaptive genetic algorithm versus Classical genetic algorithm Best Fitness Total Number of Individuals Conclusion Points to remember Questions Key terms 13. Improving Performance Structure Objectives 13.1 Calculating fitness function once 13.2 Fitness function caching 13.3 Coarsening values of genes 13.4 Parallel computing 13.5 Population snapshot Conclusion Index
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