Evolutionary Optimization: the µGP toolkit

  • Ernesto Sanchez
  • Massimiliano Schillaci
  • Giovanni Squillero

Table of contents

  1. Front Matter
    Pages 1-1
  2. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 1-7
  3. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 9-15
  4. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 17-25
  5. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 27-37
  6. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 39-56
  7. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 57-63
  8. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 65-70
  9. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 71-79
  10. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 81-95
  11. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 97-101
  12. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 103-124
  13. Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
    Pages 125-151
  14. Back Matter
    Pages 145-145

About this book

Introduction

This book describes an award-winning evolutionary algorithm that outperformed experts and conventional heuristics in solving several industrial problems. It presents a discussion of the theoretical and practical aspects that enabled μGP (MicroGP) to autonomously find the optimal solution of hard problems, handling highly structured data, such as full-fledged assembly programs, with functions and interrupt handlers.

For a practitioner, μGP is simply a versatile optimizer to tackle most problems with limited setup effort. The book is valuable for all who require heuristic problem-solving methodologies, such as engineers dealing with verification and test of electronic circuits; or researchers working in robotics and mobile communication. Examples are provided to guide the reader through the process, from problem definition to gathering results.

For an evolutionary computation researcher, μGP may be regarded as a platform where new operators and strategies can be easily tested.

MicroGP (the toolkit) is an active project hosted by Sourceforge: http://ugp3.sourceforge.net/

Keywords

Evolutionary Optimization Genetic Programming Graph-based representation Industrial Problems MicroGP Problem Complexity Real-world Applications Turing-complete evolutionary algorithm evolutionary computation

Authors and affiliations

  • Ernesto Sanchez
    • 1
  • Massimiliano Schillaci
    • 2
  • Giovanni Squillero
    • 3
  1. 1.Dip. Automatica e InformaticaPolitecnico di TorinoTorinoItaly
  2. 2.ICT ConsultantTorinoItaly
  3. 3.Dip. Automatica e InformaticaPolitecnico di TorinoTorinoItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-09426-7
  • Copyright Information Springer Science+Business Media, LLC 2011
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-09425-0
  • Online ISBN 978-0-387-09426-7