Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities

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.

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
13. Improving Performance

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/