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.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Code in Action
Get in touch
1
Section 1: The Basics of Genetic Algorithms
Section 1: The Basics of Genetic Algorithms
Free Chapter
2
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
What are genetic algorithms?
The theory behind genetic algorithms
Differences from traditional algorithms
Advantages of genetic algorithms
Limitations of genetic algorithms
Use cases of genetic algorithms
Summary
Further reading
3
Understanding the Key Components of Genetic Algorithms
Understanding the Key Components of Genetic Algorithms
Basic flow of a genetic algorithm
Selection methods
Crossover methods
Mutation methods
Real-coded genetic algorithms
Understanding elitism
Niching and sharing
The art of solving problems using genetic algorithms
Summary
Further reading
4
Section 2: Solving Problems with Genetic Algorithms
Section 2: Solving Problems with Genetic Algorithms
5
Using the DEAP Framework
Using the DEAP Framework
Technical requirements
Introduction to DEAP
Using the creator module
Using the Toolbox class
The OneMax problem
Solving the OneMax problem with DEAP
Using built-in algorithms
Experimenting with the algorithm's settings
Summary
Further reading
6
Combinatorial Optimization
Combinatorial Optimization
Technical requirements
Search problems and combinatorial optimization
Solving the knapsack problem
Solving the TSP
Solving the VRP
Summary
Further reading
7
Constraint Satisfaction
Constraint Satisfaction
Technical requirements
Constraint satisfaction in search problems
Solving the N-Queens problem
Solving the nurse scheduling problem
Solving the graph coloring problem
Summary
Further reading
8
Optimizing Continuous Functions
Optimizing Continuous Functions
Technical requirements
Chromosomes and genetic operators for real numbers
Using DEAP with continuous functions
Optimizing the Eggholder function
Optimizing Himmelblau's function
Simionescu's function and constrained optimization
Summary
Further reading
9
Section 3: Artificial Intelligence Applications of Genetic Algorithms
Section 3: Artificial Intelligence Applications of Genetic Algorithms
10
Enhancing Machine Learning Models Using Feature Selection
Enhancing Machine Learning Models Using Feature Selection
Technical requirements
Supervised machine learning
Feature selection in supervised learning
Selecting the features for the Friedman-1 regression problem
Selecting the features for the classification Zoo dataset
Summary
Further reading
11
Hyperparameter Tuning of Machine Learning Models
Hyperparameter Tuning of Machine Learning Models
Technical requirements
Hyperparameters in machine learning
Tuning the hyperparameters using a genetic grid search
Tuning the hyperparameters using a direct genetic approach
Summary
Further reading
12
Architecture Optimization of Deep Learning Networks
Architecture Optimization of Deep Learning Networks
Technical requirements
Artificial neural networks and deep learning
Optimizing the architecture of a deep learning classifier
Combining architecture optimization with hyperparameter tuning
Summary
Further reading
13
Reinforcement Learning with Genetic Algorithms
Reinforcement Learning with Genetic Algorithms
Technical requirements
Reinforcement learning
OpenAI Gym
Solving the MountainCar environment
Solving the CartPole environment
Summary
Further reading
14
Section 4: Related Technologies
Section 4: Related Technologies
15
Genetic Image Reconstruction
Genetic Image Reconstruction
Technical requirements
Reconstructing images with polygons
Image processing in Python
Using genetic algorithms to reconstruct images
Summary
Further reading
16
Other Evolutionary and Bio-Inspired Computation Techniques
Other Evolutionary and Bio-Inspired Computation Techniques
Technical requirements
Evolutionary computation and bio-inspired computing
Genetic programming
Particle swarm optimization
Other related techniques
Summary
Further reading
17
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The OneMax problem

The OneMax (or One-Max) problem is a simple optimization task that is often used as the Hello World of genetic algorithm frameworks. We will use this problem for the rest of this chapter to demonstrate how DEAP can be used to implement a genetic algorithm.

The OneMax task is to find the binary string of a given length that maximizes the sum of its digits. For example, the OneMax problem of length 5 will consider candidates such as the following:

  • 10010 (sum of digits = 2)
  • 01110 (sum of digits = 3)
  • 11111 (sum of digits = 5)

Obviously (to us), the solution to this problem is always the string that comprises all 1s. But the genetic algorithm does not have this knowledge, and needs to blindly look for this solution using its genetic operators. If the algorithm does its job, it will find this solution, or at least one close to it, within a reasonable amount of time...

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