A field guide to genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Book{poli08:fieldguide,

• author = "Riccardo Poli and William B. Langdon and Nicholas Freitag McPhee",
• title = "A field guide to genetic programming",
• publisher = "Published via \texttt{http://lulu.com} and freely available at \texttt{http://www.gp-field-guide.org.uk}",
• year = "2008",
• note = "(With contributions by J. R. Koza)",
• URL = "http://www.gp-field-guide.org.uk",
• URL = "http://www.lulu.com/shop/riccardo-poli-and-william-b-langdon-and-nicholas-freitag-mcphee/a-field-guide-to-genetic-programming/ebook/product-17447670.html",
• URL = "http://dces.essex.ac.uk/staff/rpoli/gp-field-guide/A_Field_Guide_to_Genetic_Programming.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/poli08_fieldguide.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/poli08_fieldguide.azw3",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/poli08_fieldguide.epub",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/poli08_fieldguide.mobi.zip",
• URL = "https://itunes.apple.com/gb/book/field-guide-to-genetic-programming/id445393241?mt=11",
• URL = "http://dl.acm.org/citation.cfm?id=1796422",
• URL = "https://digitalcommons.morris.umn.edu/cgi/viewcontent.cgi?article=1001&context=cs_facpubs",
• video_url = "http://youtu.be/ftjci2XJbFA",
• code_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, automatic programming, machine learning, artificial intelligence, evolutionary computation, GPU",
• isbn13 = "978-1-4092-0073-4",
• abstract = "Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions.

  This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

  Table of Contents

  1 Introduction

  1.1 Genetic Programming in a Nutshell

  1.2 Getting Started

  1.3 Prerequisites

  1.4 Overview of this Field Guide

  Part I Basics

  2 Representation, Initialisation and Operators in Tree-based GP

  2.1 Representation

  2.2 Initialising the Population

  2.3 Selection

  2.4 Recombination and Mutation",

• abstract = "

  3 Getting Ready to Run Genetic Programming

  3.1 Step 1: Terminal Set

  3.2 Step 2: Function Set

  3.2.1 Closure

  3.2.2 Sufficiency

  3.2.3 Evolving Structures other than Programs

  3.3 Step 3: Fitness Function

  3.4 Step 4: GP Parameters

  3.5 Step 5: Termination and solution designation

  4 Example Genetic Programming Run 29

  4.1 Preparatory Steps

  4.2 Step-by-Step Sample Run

  4.2.1 Initialisation

  4.2.2 Fitness Evaluation

  4.2.3 Selection, Crossover and Mutation

  4.2.4 Termination and Solution Designation

  Part II Advanced Genetic Programming

  5 Alternative Initialisations and Operators in Tree-based GP

  5.1 Constructing the Initial Population

  5.1.1 Uniform Initialisation

  5.1.2 Initialisation may Affect Bloat

  5.1.3 Seeding

  5.2 GP Mutation

  5.2.1 Is Mutation Necessary?

  5.2.2 Mutation Cookbook

  5.3 GP Crossover

  5.4 Other Techniques

  6 Modular, Grammatical and Developmental Tree-based GP

  6.1 Evolving Modular and Hierarchical Structures

  6.1.1 Automatically Defined Functions

  6.1.2 Program Architecture and Architecture-Altering

  6.2 Constraining Structures

  6.2.1 Enforcing Particular Structures

  6.2.2 Strongly Typed GP

  6.2.3 Grammar-based Constraints

  6.2.4 Constraints and Bias

  6.3 Developmental Genetic Programming

  6.4 Strongly Typed Autoconstructive GP with PushGP

  7 Linear and Graph Genetic Programming

  7.1 Linear Genetic Programming

  7.1.1 Motivations

  7.1.2 Linear GP Representations

  7.1.3 Linear GP Operators

  7.2 Graph-Based Genetic Programming

  7.2.1 Parallel Distributed GP (PDGP)

  7.2.2 PADO

  7.2.3 Cartesian GP

  7.2.4 Evolving Parallel Programs using Indirect Encodings

  8 Probabilistic Genetic Programming

  8.1 Estimation of Distribution Algorithms

  8.2 Pure EDA GP

  8.3 Mixing Grammars and Probabilities

  9 Multi-objective Genetic Programming

  9.1 Combining Multiple Objectives into a Scalar Fitness Function

  9.2 Keeping the Objectives Separate

  9.2.1 Multi-objective Bloat and Complexity Control

  9.2.2 Other Objectives

  9.2.3 Non-Pareto Criteria

  9.3 Multiple Objectives via Dynamic and Staged Fitness Functions

  9.4 Multi-objective Optimisation via Operator Bias

  10 Fast and Distributed Genetic Programming

  10.1 Reducing Fitness Evaluations/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

  10.4.1 Master slave GP

  10.4.2 GP Running on GPUs

  10.4.3 GP on FPGAs

  10.4.4 Sub-machine-code GP

  10.5 Geographically Distributed GP

  11 GP Theory and its Applications

  11.1 Mathematical Models

  11.2 Search Spaces

  11.3 Bloat

  11.3.1 Bloat in Theory

  11.3.2 Bloat Control in Practice

  Part III Practical Genetic Programming

  12 Applications

  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, 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 Entertainment and Computer Games

  12.10 The Arts

  12.11 Compression

  13 Troubleshooting GP

  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

  14 Conclusions

  Part IV Tricks of the Trade

  A Resources

  A.1 Key Books

  A.2 Key Journals

  A.3 Key International Meetings

  A.4 GP Implementations

  A.5 On-Line Resources

  B TinyGP

  B.1 Overview of TinyGP

  B.2 Input Data Files for TinyGP

  B.3 Source Code

  B.4 Compiling and Running TinyGP

  Bibliography

  Index",

• notes = "http://www.gp-field-guide.org.uk/

  Video of writing the book http://youtu.be/ftjci2XJbFA

  Added March 2020 http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/tiny_gp",

• size = "250 pages",

}

Genetic Programming entries for Riccardo Poli William B Langdon Nicholas Freitag McPhee

Citations