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Genetic Algorithms in Java Basics

Book Description

Genetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language specific issues and concepts involved with genetic algorithms and you'll have everything you need to start building your own.

Genetic algorithms are frequently used to solve highly complex real world problems and with this book you too can harness their problem solving capabilities. Understanding how to utilize and implement genetic algorithms is an essential tool in any respected software developers toolkit. So step into this intriguing topic and learn how you too can improve your software with genetic algorithms, and see real Java code at work which you can develop further for your own projects and research.

  • Guides you through the theory behind genetic algorithms
  • Explains how genetic algorithms can be used for software developers trying to solve a range of problems
  • Provides a step-by-step guide to implementing genetic algorithms in Java
  • Table of Contents

    1. Cover
    2. Title
    3. Copyright
    4. Contents at a Glance
    5. Contents
    6. About the Authors
    7. About the Technical Reviewers
    8. Preface
    9. Chapter 1: Introduction
      1. What is Artificial Intelligence?
      2. Biologically Analogies
      3. History of Evolutionary Computation
      4. The Advantage of Evolutionary Computation
      5. Biological Evolution
        1. An Example of Biological Evolution
      6. Basic Terminology
        1. Terms
      7. Search Spaces
        1. Fitness Landscapes
        2. Local Optimums
      8. Parameters
        1. Mutation Rate
        2. Population Size
        3. Crossover Rate
      9. Genetic Representations
      10. Termination
      11. The Search Process
      12. CITATIONS
    10. Chapter 2:Implementation of a Basic Genetic Algorithm
      1. Pre-Implementation
      2. Pseudo Code for a Basic Genetic Algorithm
      3. About the Code Examples in this Book
      4. Basic Implementation
        1. The Problem
        2. Parameters
        3. Initialization
        4. Evaluation
        5. Termination Check
        6. Crossover
        7. Elitism
        8. Mutation
        9. Execution
      5. Summary
    11. Chapter 3: Robotic Controllers
      1. Introduction
      2. The Problem
      3. Implementation
        1. Before You Start
        2. Encoding
        3. Initialization
        4. Evaluation
        5. Termination Check
        6. Selection Method and Crossover
        7. Execution
      4. Summary
        1. Exercises
    12. Chapter 4: Traveling Salesman
      1. Introduction
      2. The Problem
      3. Implementation
        1. Before You Start
        2. Encoding
        3. Initialization
        4. Evaluation
        5. Termination Check
        6. Crossover
        7. Mutation
        8. Execution
      4. Summary
        1. Exercises
    13. Chapter 5: Class Scheduling
      1. Introduction
      2. The Problem
      3. Implementation
        1. Before You Start
        2. Encoding
        3. Initialization
        4. The Executive Class
        5. Evaluation
        6. Termination
        7. Mutation
        8. Execution
      4. Analysis and Refinement
        1. Exercises
      5. Summary
    14. Chapter 6: Optimization
      1. Adaptive Genetic Algorithms
        1. Implementation
        2. Exercises
      2. Multi-Heuristics
        1. Implementation
        2. Exercises
      3. Performance Improvements
        1. Fitness Function Design
        2. Parallel Processing
        3. Fitness Value Hashing
        4. Encoding
        5. Mutation and Crossover Methods
      4. Summary
    15. Index