Autonomous Search

  • Youssef Hamadi
  • Eric Monfroy
  • Frédéric Saubion

Table of contents

  1. Front Matter
    Pages I-XV
  2. Youssef Hamadi, Eric Monfroy, Frédéric Saubion
    Pages 1-11
  3. Off-line Configuration

    1. Front Matter
      Pages 13-13
    2. Derek Bridge, Eoin O’Mahony, Barry O’Sullivan
      Pages 73-95
    3. Susan L. Epstein, Smiljana Petrovic
      Pages 97-127
  4. On-line Control

    1. Front Matter
      Pages 129-129
    2. Roberto Battiti, Paolo Campigotto
      Pages 131-160
    3. Jorge Maturana, Álvaro Fialho, Frédéric Saubion, Marc Schoenauer, Frédéric Lardeux, Michèle Sebag
      Pages 161-189
    4. Thomas Stützle, Manuel López-Ibáñez, Paola Pellegrini, Michael Maur, Marco Montes de Oca, Mauro Birattari et al.
      Pages 191-215
  5. New Directions and Applications

    1. Front Matter
      Pages 217-217
    2. Alejandro Arbelaez, Youssef Hamadi, Michèle Sebag
      Pages 219-243
    3. Youssef Hamadi, Said Jabbour, Jabbour Sais
      Pages 245-267
    4. Marek Petrik, Shlomo Zilberstein
      Pages 269-305

About this book

Introduction

Decades of innovations in combinatorial problem solving have produced better and more complex algorithms. These new methods are better since they can solve larger problems and address new application domains. They are also more complex which means that they are hard to reproduce and often harder to fine-tune to the peculiarities of a given problem. This last point has created a paradox where efficient tools are out of reach of practitioners.

 

Autonomous search (AS) represents a new research field defined to precisely address the above challenge. Its major strength and originality consist in the fact that problem solvers can now perform self-improvement operations based on analysis of the performances of the solving process -- including short-term reactive reconfiguration and long-term improvement through self-analysis of the performance, offline tuning and online control, and adaptive control and supervised control. Autonomous search "crosses the chasm" and provides engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems.

 

This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms.

Keywords

Adaptation Artificial life Case-based reasoning (CBR) Combinatorial search Constraint programming Constraint solving Control theory Evolutionary computing Heuristics Hyperheuristics Machine learning Planning SAT

Editors and affiliations

  • Youssef Hamadi
    • 1
  • Eric Monfroy
    • 2
  • Frédéric Saubion
    • 3
  1. 1.Microsoft Research CambridgeCambridgeUnited Kingdom
  2. 2.Federico Santa María, Departamento de InformáticaUniversidad TécnicaValparaísoChile
  3. 3.Faculté des Sciences, LERIAUniversité d'AngersAngers CX 01France

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-21434-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-21433-2
  • Online ISBN 978-3-642-21434-9
  • About this book