Genetic Algorithms in Java Basics 1st edition by Lee Jacobson, Burak Kanber ISBN 1484203283 9781484203286

Chapter 1: Introduction

• What is Artificial Intelligence?
• Biological Analogies
• History of Evolutionary Computation
• The Advantage of Evolutionary Computation
• Biological Evolution
• An Example of Biological Evolution
• Basic Terminology
  • Terms
  • Search Spaces
  • Fitness Landscapes
  • Local Optimums
  • Parameters
  • Mutation Rate
  • Population Size
  • Crossover Rate
  • Genetic Representations
  • Termination
• The Search Process
• CITATIONS

Chapter 2: Implementation of a Basic Genetic Algorithm

• Pre-Implementation
• Pseudo Code for a Basic Genetic Algorithm
• About the Code Examples in this Book
• Basic Implementation
  • The Problem
  • Parameters
  • Initialization
  • Evaluation
  • Termination Check
  • Crossover
  • Roulette Wheel Selection
  • Crossover Methods
  • Crossover Pseudo Code
  • Crossover Implementation
  • Elitism
  • Mutation
  • Execution
• Summary

Chapter 3: Robotic Controllers

• Introduction
• The Problem
• Implementation
  • Before You Start
  • Encoding
  • Initialization
  • Evaluation
  • Termination Check
  • Selection Method and Crossover
  • Tournament Selection
  • Single Point Crossover
  • Execution
• Summary
• Exercises

Chapter 4: Traveling Salesman

• Introduction
• The Problem
• Implementation
  • Before You Start
  • Encoding
  • Initialization
  • Evaluation
  • Termination Check
  • Crossover
  • Mutation
  • Execution
• Summary
• Exercises

Chapter 5: Class Scheduling

• Introduction
• The Problem
• Implementation
  • Before You Start
  • Encoding
  • Initialization
  • The Executive Class
  • Evaluation
  • Termination
  • Mutation
  • Execution
  • Analysis and Refinement
• Exercises
• Summary

Chapter 6: Optimization

• Adaptive Genetic Algorithms
  • Implementation
  • Exercises
• Multi-Heuristics
  • Implementation
  • Exercises
• Performance Improvements
  • Fitness Function Design
  • Parallel Processing
  • Fitness Value Hashing
• Encoding
• Mutation and Crossover Methods