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.

• Cover Page
• Title Page
• Copyright Page
• Dedication Page
• About the Author
• About the Reviewer
• Acknowledgement
• Preface
• Errata
• Table of Contents
• 1. Introduction
  • Structure
  • 1.1 Nature of genetic algorithm
  • 1.2 Applicability of genetic algorithms
  • 1.3 Pros and cons of genetic algorithms
  • 1.4 Your first genetic algorithm
  • Conclusion
  • Questions
• 2. Genetic Algorithm Flow
  • Structure
  • 2.1 Individual
  • 2.2 Fitness function
  • 2.3 Population
  • 2.4 Selection
  • 2.5 Crossover
  • 2.6 Mutation
  • 2.7 Genetic algorithm flow
  • Conclusion
  • Points to remember:
  • Multiple choice questions:
    • Answers
  • Questions
  • Key terms
• 3. Selection
  • Structure
  • Objectives
  • 3.1 Tournament selection
  • 3.2 Proportional selection
  • 3.3 Stochastic universal sampling selection
  • 3.4 Rank selection
  • 3.5 Elite selection
  • Conclusion
  • Points to remember
  • Multiple choice questions
    • Answers
  • Key terms
• 4. Crossover
  • Structure
  • Objectives
  • 4.1 One-point crossover
  • 4.2 N-point crossover
  • 4.3 Uniform crossover
  • 4.4 Linear combination crossover
  • 4.5 Blend crossover
  • 4.6 Order crossover
  • 4.7 Fitness driven crossover
  • Conclusion
  • Points to remember
  • Multiple choice questions
    • Answers
  • Questions
  • Key terms
• 5. Mutation
  • Structure
  • Objectives
  • 5.1 Random deviation mutation
    • Random deviation mutation
  • 5.2 Exchange mutation
  • 5.3 Shift mutation
  • 5.4 Bit flip mutation
  • 5.5 Inversion mutation
  • 5.6 Shuffle mutation
  • 5.7 Fitness driven mutation
  • Conclusion
  • Points to remember
  • Multiple choice questions
    • Answers
  • Questions
  • Key terms
• 6. Effectiveness
  • Structure
  • Objectives
  • 6.1 Best individual
  • 6.2 Total number of individuals
  • 6.3 Genetic algorithm as random variable
  • 6.4 Monte-Carlo simulation
  • Conclusion
  • Points to remember
  • Multiple choice questions
    • Answers
  • Key terms
• 7. Parameter Tuning
  • Structure
  • Objectives
  • 7.1 Population size
  • 7.2 Crossover probability
  • 7.3 Mutation probability
  • Conclusion
  • Points to remember
  • Multiple choice questions
    • Answers
  • Questions
  • Key terms
• 8. Black-Box Function
  • Structure
  • Objectives
  • 8.1 What is Black-box function?
  • 8.2 Gene encodings
  • 8.3 Genetic algorithm architecture
  • Conclusion
  • Points to remember
  • Multiple choice questions
    • Answers
  • Questions
  • Key terms
• 9. Combinatorial Optimization – Binary Gene Encoding
  • Structure
  • Objectives
  • 9.1 Knapsack problem
  • 9.2 Schedule problem
  • 9.3 Radar placement problem
  • Conclusion
  • Questions
• 10. Combinatorial Optimization – Ordered Gene Encoding
  • Structure
  • Objectives
  • 10.1 Travelling Salesman Problem
  • 10.2 Football manager problem
  • Conclusion
  • Questions
• 11. Other Common Problems
  • Structure
  • Objective
  • 11.1 System of equations
  • 11.2 Graph coloring problem
  • Conclusion
  • Questions
• 12. Adaptive Genetic Algorithm
  • Structure
  • Objectives
  • 12.1 Evolutionary improvement rate
  • 12.2 Evolutionary progress and population size
  • 12.3 Evolutionary progress, crossover, and mutation probabilities
  • 12.4 Evolutionary dead-end and premature termination of the genetic algorithm
  • 12.5 Example of adaptive genetic algorithm
  • 12.6 Adaptive genetic algorithm versus Classical genetic algorithm
  • Best Fitness
  • Total Number of Individuals
  • Conclusion
  • Points to remember
  • Questions
  • Key terms
• 13. Improving Performance
  • Structure
  • Objectives
  • 13.1 Calculating fitness function once
  • 13.2 Fitness function caching
  • 13.3 Coarsening values of genes
  • 13.4 Parallel computing
  • 13.5 Population snapshot
  • Conclusion
• Index