Chapter 1, An Introduction to Genetic Algorithms, introduces genetic algorithms, their underlying theory, and their basic principles of operation. You will then explore the differences between genetic algorithms and traditional methods, and learn about the best use cases for genetic algorithms.
Chapter 2, Understanding the Key Components of Genetic Algorithms, dives deeper into the key components and the implementation details of genetic algorithms. After outlining the basic genetic flow, you will learn about their different components and the various implementations for each component.
Chapter 3, Using the DEAP Framework, introduces DEAP—a powerful and flexible evolutionary computation framework capable of solving real-life problems using genetic algorithms. You will discover how to use this framework by writing a Python program that solves the OneMax problem—the 'Hello World' of genetic algorithms.
Chapter 4, Combinatorial Optimization, covers combinatorial optimization problems, such as the knapsack problem, the traveling salesman problem, and the vehicle routing problem, and how to write Python programs that solve them using genetic algorithms and the DEAP framework.
Chapter 5, Constraint Satisfaction, introduces constraint satisfaction problems, such as the N-Queen problem, the nurse scheduling problem, and the graph coloring problem, and explains how to write Python programs that solve them using genetic algorithms and the DEAP framework.
Chapter 6, Optimizing Continuous Functions, covers continuous optimization problems, and how they can be solved by means of genetic algorithms. The examples you will use include the optimization of the Eggholder function, Himmelblau's function, and Simionescu's function. Along the way, you will explore the concepts of niching, sharing, and constraint handling.
Chapter 7, Enhancing Machine Learning Models Using Feature Selection, talks about supervised machine learning models, and explains how genetic algorithms can be used to improve the performance of these models by selecting the best subset of features from the input data provided.
Chapter 8, Hyperparameter Tuning of Machine Learning Models, explains how genetic algorithms can be used to improve the performance of supervised machine learning models by tuning the hyperparameters of the models, either by applying a genetic algorithm-driven grid search, or by using a direct genetic search.
Chapter 9, Architecture Optimization of Deep Learning Networks, focuses on artificial neural networks, and discovers how genetic algorithms can be used to improve the performance of neural-based models by optimizing their network architecture. You will then learn how to combine network architecture optimization with hyperparameter tuning.
Chapter 10, Reinforcement Learning with Genetic Algorithms, covers reinforcement learning, and explains how genetic algorithms can be applied to reinforcement learning tasks while solving two benchmark environments—MountainCar and CartPole— from the OpenAI Gym toolkit.
Chapter 11, Genetic Image Reconstruction, experiments with the reconstruction of a well-known image using a set of semi-transparent polygons, orchestrated by genetic algorithms. Along the way, you will gain useful experience in image processing and the relevant Python libraries.
Chapter 12, Other Evolutionary and Bio-Inspired Computation Techniques, broadens your horizons and gets you acquainted with several other biologically inspired problem-solving techniques. Two of these methods—genetic programming and particle swarm optimization—will be demonstrated using DEAP-based Python programs.