Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

  • Germán Terrazas
  • Dario Landa-Silva
  • Natalio Krasnogor
Part of the Studies in Computational Intelligence book series (SCI, volume 284)

Abstract

The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for specific problems, that is, the input to the algorithm are problems and the output are problem-tailored heuristics. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Thus, hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem in hand. Some approaches like genetic programming have been proposed for this. In this paper, we report on an alternative methodology that sheds light on simple methodologies that efficiently cooperate by means of local interactions. These entities are seen as building blocks, the combination of which is employed for the automated manufacture of good performing heuristic search strategies.We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Germán Terrazas
    • 1
  • Dario Landa-Silva
    • 1
  • Natalio Krasnogor
    • 1
  1. 1.ASAP Group, School of Computer ScienceUniversity of NottinghamUK

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