Table of Contents

• Acknowledgements

• 1.1 Genetic Programming in a Nutshell
• 1.2 Getting Started
• 1.3 Prerequisites
• 1.4 Overview of this Field Guide

• 2.1 Representation
• 2.2 Initialising the Population
• 2.3 Selection
• 2.4 Recombination and Mutation

• 3.1 Step 1: Terminal Set
• 3.2 Step 2: Function Set
• 3.3 Step 3: Fitness Function
• 3.4 Step 4: GP Parameters
• 3.5 Step 5: Termination and solution designation

• 4.1 Preparatory Steps
• 4.2 Step-by-Step Sample Run

• 5.1 Constructing the Initial Population
• 5.2 GP Mutation
• 5.3 GP Crossover
• 5.4 Other Techniques

• 6.1 Evolving Modular and Hierarchical Structures
• 6.2 Constraining Structures
• 6.3 Developmental Genetic Programming
• 6.4 Strongly Typed Autoconstructive GP with PushGP

• 7.1 Linear Genetic Programming
• 7.2 Graph-Based Genetic Programming

• 8.1 Estimation of Distribution Algorithms
• 8.2 Pure EDA GP
• 8.3 Mixing Grammars and Probabilities

• 9.1 Combining Multiple Objectives into a Scalar Fitness Function
• 9.2 Keeping the Objectives Separate
• 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions
• 9.4 MO GP via Operator Bias

• 10.1 Reducing Fitness Evaluations and/or Increasing their Effectiveness
• 10.2 Reducing Cost of Fitness with Caches
• 10.3 Parallel and Distributed GP are Not Equivalent
• 10.4 Running GP on Parallel Hardware
• 10.5 Geographically Distributed GP

• 11.1 Mathematical Models
• 11.2 Search Spaces
• 11.3 Bloat

• 12.1 Where GP has Done Well
• 12.2 Curve Fitting, Data Modelling and Symbolic Regression
• 12.3 Human Competitive Results: The Humies
• 12.4 Image and Signal Processing
• 12.5 Financial Trading, Time Series Prediction and Economic Modelling
• 12.6 Industrial Process Control
• 12.7 Medicine, Biology and Bioinformatics
• 12.8 GP to Create Searchers and Solvers - Hyper-heuristics
• 12.9 3/4 Entertainment and Computer Games
• 12.10 The Arts
• 12.11 Compression

• 13.1 Is there a Bug in the Code?
• 13.2 Can you Trust your Results?
• 13.3 There are No Silver Bullets
• 13.4 Small Changes can have Big Effects
• 13.5 Big Changes can have No Effect
• 13.6 Study your Populations
• 13.7 Encourage Diversity
• 13.8 Embrace Approximation
• 13.9 Control Bloat
• 13.10 Checkpoint Results
• 13.11 Report Well
• 13.12 Convince your Customers

• A.1 Key Books
• A.2 Key Journals
• A.3 Key International Meetings
• A.4 GP Implementations
• A.5 On-Line Resources

• B.1 Overview of TinyGP
• B.2 Input Data Files for TinyGP
• B.3 Source Code
• B.4 Compiling and Running TinyGP