Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorithm
- Length: 270 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2021-02-13
- ISBN-10: 8194837758
- ISBN-13: 9788194837756
- Sales Rank: #1176772 (See Top 100 Books)
Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions
Key Features
- 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.
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.
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
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