Genetic Programming an introduction 1st Edition by Wolfgang Banzhaf, Peter Nordin, Robert Keller, Frank Francone ISBN 1493303570 9781493303571

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ISBN 10: 1493303570
ISBN 13: 9781493303571
Author: Wolfgang Banzhaf, Peter Nordin, Robert Keller, Frank Francone

Since the early 1990s, genetic programming (GP)―a discipline whose goal is to enable the automatic generation of computer programs―has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin’s theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks.

This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

Genetic Programming an introduction 1st Edition Table of contents:

Part I: Introduction to Genetic Programming

1. Introduction to Genetic Programming

   • Overview of Genetic Programming
   • Historical Development and Foundations
   • Key Concepts in Evolutionary Algorithms
   • Genetic Algorithms vs. Genetic Programming

2. Evolutionary Computation

   • The Evolutionary Computation Paradigm
   • Genetic Algorithms and Evolutionary Strategies
   • Evolutionary Programming vs. Genetic Programming
   • The Role of Selection, Crossover, and Mutation in Evolution

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Part II: Representations and Evolutionary Structures

3. Representation of Solutions in Genetic Programming

   • Tree-based Representation of Programs
   • Encoding Functions and Terminals
   • Constraints on Representation and Design Choices
   • Symbolic Regression as a GP Task

4. Genetic Operations in GP

   • Selection Mechanisms: Fitness Proportional, Tournament, and Rank Selection
   • Crossover and Mutation Operators
   • Reproduction and Elitism
   • Generational vs. Steady-State Models

5. Fitness Evaluation and Objective Functions

   • Defining Fitness in Genetic Programming
   • Evaluation Criteria and Objective Functions
   • Handling Overfitting and Generalization
   • Multi-objective Optimization in GP

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Part III: Algorithms and Techniques

6. Genetic Programming Algorithms

   • Basic GP Algorithm
   • Variants of GP: Syntactic and Semantic GP
   • Tree-based vs. Linear GP
   • Parallel and Distributed GP Algorithms

7. Tree Search and Optimization

   • Tree Search Methods in Genetic Programming
   • Depth and Size of Trees in Evolutionary Search
   • Techniques to Promote Balanced Trees
   • Pruning and Post-processing of Programs

8. Hybrid Genetic Programming

   • Combining GP with Other Evolutionary Techniques
   • Memetic Algorithms and GP
   • Neurogenetic Systems
   • Coevolutionary and Multi-agent GP Systems

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Part IV: Applications of Genetic Programming

9. Genetic Programming in Function Optimization

   • Symbolic Regression and Mathematical Modeling
   • Evolving Mathematical Expressions for Data Fitting
   • Applications in Financial Modeling and Forecasting

10. Automated Design and Engineering Applications

    • Evolving Controllers and Systems
    • Genetic Programming for Hardware Design
    • Applications in Robotics and Automation

11. Data Mining and Machine Learning with GP

    • GP for Classification and Regression Tasks
    • Feature Selection and Dimensionality Reduction
    • Applications in Pattern Recognition and Classification

12. Genetic Programming in Artificial Intelligence

    • Symbolic Learning with GP
    • Evolutionary Robotics and Autonomous Agents
    • GP for Game Playing and Strategy Optimization

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Part V: Advanced Topics and Challenges

13. Challenges in Genetic Programming

    • Scalability and Efficiency Issues in GP
    • The Bloat Problem and Solutions
    • Convergence and Exploration Balance
    • Handling Noisy and Complex Fitness Landscapes

14. Theory and Analysis of Genetic Programming

    • Theoretical Foundations of GP
    • Formal Properties and Limits of GP
    • Algorithmic and Computational Complexity
    • Theoretical Models of GP Behavior

15. Future Directions and Open Problems in GP

    • Recent Advances and Innovations in GP
    • Genetic Programming in the Context of Other AI Techniques
    • Interdisciplinary Applications of GP
    • Emerging Trends in Evolutionary Computation

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Conclusion
16. Conclusion and Summary
– Recap of Key Concepts and Techniques
– The State of the Art in Genetic Programming
– The Future of Genetic Programming Research
– Final Thoughts on GP’s Role in Evolutionary Computation

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