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Boosting Metaheuristic Search Using Reinforcement Learning

  • Tony Wauters
  • Katja Verbeeck
  • Patrick De Causmaecker
  • Greet Vanden Berghe
Part of the Studies in Computational Intelligence book series (SCI, volume 434)

Abstract

Many techniques that boost the speed or quality of metaheuristic search have been reported within literature. The present contribution investigates the rather rare combination of reinforcement learning and metaheuristics. Reinforcement learning techniques describe how an autonomous agent can learn from experience. Previous work has shown that a network of simple reinforcement learning devices based on learning automata can generate good heuristics for (multi) project scheduling problems. However, using reinforcement learning to generate heuristics is just one method of how reinforcement learning can strengthen metaheuristic search. Both existing and new methodologies to boost metaheuristics using reinforcement learning are presented together with experiments on actual benchmarks.

Keywords

Schedule Problem Reinforcement Learn Multiagent System Learn Automaton Project Schedule Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tony Wauters
    • 1
  • Katja Verbeeck
    • 2
  • Patrick De Causmaecker
    • 3
  • Greet Vanden Berghe
    • 4
  1. 1.CODeS, KAHO Sint-LievenGentBelgium
  2. 2.Vakgroep ICTUniversity College, Katholieke Hogeschool Sint-LievenGhentBelgium
  3. 3.Katholieke Universiteit LeuvenKortrijkBelgium
  4. 4.CODeS (Combinatorial Optimisation and Decision Support) Industrial SciencesLievenBelgium

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