Using this book, you will gain expertise in genetic algorithms, understand how they work and know when and how to use them to create intelligent Python-based applications. By the end of this book, you will have hands-on experience applying genetic algorithms to artificial intelligence as well as numerous other domains.

• Cover
• Title Page
• Copyright and Credits
• Dedication
• About Packt
• Contributors
• Table of Contents
• Preface
• Section 1: The Basics of Genetic Algorithms
• Chapter 1: An Introduction to Genetic Algorithms
  • What are genetic algorithms?
    • Darwinian evolution
    • The genetic algorithms analogy
      • Genotype
      • Population
      • Fitness function
      • Selection
      • Crossover
      • Mutation
  • The theory behind genetic algorithms
    • The schema theorem
  • Differences from traditional algorithms
    • Population-based
    • Genetic representation
    • Fitness function
    • Probabilistic behavior
  • Advantages of genetic algorithms
    • Global optimization
    • Handling complex problems 
    • Handling a lack of mathematical representation
    • Resilience to noise
    • Parallelism
    • Continuous learning
  • Limitations of genetic algorithms
    • Special definitions
    • Hyperparameter tuning
    • Computationally-intensive
    • Premature convergence
    • No guaranteed solution
  • Use cases of genetic algorithms
  • Summary
  • Further reading
• Chapter 2: Understanding the Key Components of Genetic Algorithms
  • Basic flow of a genetic algorithm
    • Creating the initial population
    • Calculating the fitness
    • Applying selection, crossover, and mutation
    • Checking the stopping conditions
  • Selection methods
    • Roulette wheel selection
    • Stochastic universal sampling
    • Rank-based selection
    • Fitness scaling
    • Tournament selection
  • Crossover methods
    • Single-point crossover
    • Two-point and k-point crossover
    • Uniform crossover
    • Crossover for ordered lists
      • Ordered crossover 
  • Mutation methods
    • Flip bit mutation
    • Swap mutation
    • Inversion mutation
    • Scramble mutation
  • Real-coded genetic algorithms
    • Blend crossover
    • Simulated binary crossover
    • Real mutation
  • Understanding elitism
  • Niching and sharing
    • Serial niching versus parallel niching
  • The art of solving problems using genetic algorithms
  • Summary
  • Further reading
• Section 2: Solving Problems with Genetic Algorithms
• Chapter 3: Using the DEAP Framework
  • Technical requirements
  • Introduction to DEAP
  • Using the creator module
    • Creating the Fitness class
      • Defining the fitness strategy
      • Storing the fitness values
    • Creating the Individual class
  • Using the Toolbox class
    • Creating genetic operators
    • Creating the population
    • Calculating the fitness
  • The OneMax problem
  • Solving the OneMax problem with DEAP
    • Choosing the chromosome
    • Calculating the fitness
    • Choosing the genetic operators
    • Setting the stopping condition
    • Implementing with DEAP
      • Setting up
      • Evolving the solution
      • Running the program
  • Using built-in algorithms
    • The Statistics object
    • The algorithm
    • The logbook
    • Running the program
    • Adding the hall of fame
  • Experimenting with the algorithm's settings
    • Population size and number of generations
    • Crossover operator
    • Mutation operator
    • Selection operator
      • Tournament size and relation to mutation probability
      • Roulette wheel selection
  • Summary
  • Further reading
• Chapter 4: Combinatorial Optimization
  • Technical requirements
  • Search problems and combinatorial optimization
  • Solving the knapsack problem
    • The Rosetta Code knapsack 0-1 problem
    • Solution representation
    • Python problem representation
    • Genetic algorithms solution
  • Solving the TSP
    • TSPLIB benchmark files
    • Solution representation
    • Python problem representation
    • Genetic algorithms solution
    • Improving the results with enhanced exploration and elitism
  • Solving the VRP
    • Solution representation
    • Python problem representation
    • Genetic algorithms solution
  • Summary
  • Further reading
• Chapter 5: Constraint Satisfaction
  • Technical requirements
  • Constraint satisfaction in search problems
  • Solving the N-Queens problem
    • Solution representation
    • Python problem representation
    • Genetic algorithms solution
  • Solving the nurse scheduling problem
    • Solution representation
    • Hard constraints versus soft constraints
    • Python problem representation
    • Genetic algorithms solution
  • Solving the graph coloring problem
    • Solution representation
    • Using hard and soft constraints for the graph coloring problem
    • Python problem representation
    • Genetic algorithms solution
  • Summary
  • Further reading
• Chapter 6: Optimizing Continuous Functions
  • Technical requirements
  • Chromosomes and genetic operators for real numbers
  • Using DEAP with continuous functions
  • Optimizing the Eggholder function
    • Optimizing the Eggholder function with genetic algorithms
    • Improving the speed with an increased mutation rate
  • Optimizing Himmelblau's function
    • Optimizing Himmelblau's function with genetic algorithms
    • Using niching and sharing to find multiple solutions
  • Simionescu's function and constrained optimization
    • Constrained optimization with genetic algorithms
    • Optimizing Simionescu's function using genetic algorithms
    • Using constraints to find multiple solutions
  • Summary
  • Further reading
• Section 3: Artificial Intelligence Applications of Genetic Algorithms
• Chapter 7: Enhancing Machine Learning Models Using Feature Selection
  • Technical requirements
  • Supervised machine learning
    • Classification
    • Regression
    • Supervised learning algorithms
  • Feature selection in supervised learning
  • Selecting the features for the Friedman-1 regression problem
    • Solution representation
    • Python problem representation
    • Genetic algorithms solution
  • Selecting the features for the classification Zoo dataset
    • Python problem representation
    • Genetic algorithms solution
  • Summary
  • Further reading
• Chapter 8: Hyperparameter Tuning of Machine Learning Models
  • Technical requirements
  • Hyperparameters in machine learning
    • Hyperparameter tuning
    • The Wine dataset
    • The adaptive boosting classifier
  • Tuning the hyperparameters using a genetic grid search
    • Testing the classifier's default performance
    • Running the conventional grid search
    • Running the genetic algorithm-driven grid search
  • Tuning the hyperparameters using a direct genetic approach
    • Hyperparameter representation
    • Evaluating the classifier accuracy
    • Tuning the hyperparameters using genetic algorithms
  • Summary
  • Further reading
• Chapter 9: Architecture Optimization of Deep Learning Networks
  • Technical requirements
  • Artificial neural networks and deep learning
    • Multilayer Perceptron
    • Deep learning and convolutional neural networks
  • Optimizing the architecture of a deep learning classifier
    • The Iris flower dataset
    • Representing the hidden layer configuration
    • Evaluating the classifier's accuracy
    • Optimizing the MLP architecture using genetic algorithms
  • Combining architecture optimization with hyperparameter tuning
    • Solution representation
    • Evaluating the classifier's accuracy
    • Optimizing the MLP's combined configuration using genetic algorithms
  • Summary
  • Further reading
• Chapter 10: Reinforcement Learning with Genetic Algorithms
  • Technical requirements
  • Reinforcement learning
    • Genetic algorithms and reinforcement learning
  • OpenAI Gym
    • The env interface
  • Solving the MountainCar environment
    • Solution representation
    • Evaluating the solution
    • Python problem representation
    • Genetic algorithms solution
  • Solving the CartPole environment
    • Controlling the CartPole with a neural network
    • Solution representation and evaluation
    • Python problem representation
    • Genetic algorithms solution
  • Summary
  • Further reading
• Section 4: Related Technologies
• Chapter 11: Genetic Image Reconstruction
  • Technical requirements
  • Reconstructing images with polygons
  • Image processing in Python
    • Python image processing libraries
      • The Pillow library
      • The scikit-image library
      • The opencv-python library
    • Drawing images with polygons
    • Measuring the difference between images
      • Pixel-based Mean Squared Error
      • Structural Similarity (SSIM)
  • Using genetic algorithms to reconstruct images
    • Solution representation and evaluation
    • Python problem representation
    • Genetic algorithm implementation
      • Adding a callback to the genetic run
    • Image reconstruction results
      • Using pixel-based Mean Squared Error
      • Using the SSIM index
      • Other experiments
  • Summary
  • Further reading
• Chapter 12: Other Evolutionary and Bio-Inspired Computation Techniques
  • Technical requirements
  • Evolutionary computation and bio-inspired computing
  • Genetic programming
    • Genetic programming example – even parity check
    • Genetic programming implementation
      • Simplifying the solution
  • Particle swarm optimization
    • PSO example – function optimization
    • Particle swarm optimization implementation
  • Other related techniques
    • Evolution strategies
    • Differential evolution
    • Ant colony optimization
    • Artificial immune systems
    • Artificial life
  • Summary
  • Further reading
• Other Books You May Enjoy
• Index