Book Image

Hands-On Genetic Algorithms with Python

By : Eyal Wirsansky
Book Image

Hands-On Genetic Algorithms with Python

By: Eyal Wirsansky

Overview of 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.
Table of Contents (18 chapters)
1
Section 1: The Basics of Genetic Algorithms
4
Section 2: Solving Problems with Genetic Algorithms
9
Section 3: Artificial Intelligence Applications of Genetic Algorithms
14
Section 4: Related Technologies

Hyperparameter Tuning of Machine Learning Models

This chapter describes how genetic algorithms can be used to improve the performance of supervised machine learning models by tuning the hyperparameters of the models. The chapter will start with a brief introduction to hyperparameter tuning in machine learning before describing the concept of a grid search. After introducing the Wine dataset and the adaptive boosting classifier, both of which will be used throughout this chapter, we will demonstrate hyperparameter tuning using both a conventional grid search and a genetic algorithm-driven grid search. Finally, we will attempt to enhance the results we get by using a direct genetic algorithm approach for hyperparameter tuning.

By the end of this chapter, you will:

  • Understand the concept of hyperparameter tuning in machine learning
  • Be familiar with the Wine dataset and the adaptive...