Discovering beneficial cooperative structures for the automated construction of heuristics

Terrazas, German; Landa-Silva, Dario; Krasnogor, Natalio

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. © 2010 Springer-Verlag Berlin Heidelberg.

Citation

Terrazas, G., Landa-Silva, D., & Krasnogor, N. (2010). Discovering beneficial cooperative structures for the automated construction of heuristics. In Nature inspired cooperative strategies for optimization (NICSO 2010) (89-100). Springer Verlag. https://doi.org/10.1007/978-3-642-12538-6_8

• Acceptance Date: Jan 26, 2010
• Online Publication Date: Apr 16, 2010
• Publication Date: Apr 27, 2010
• Deposit Date: Aug 1, 2016
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Issue: 284
• Pages: 89-100
• Series Title: Studies in Computational Intelligence
• Book Title: Nature inspired cooperative strategies for optimization (NICSO 2010)
• ISBN: 9783642125386
• DOI: https://doi.org/10.1007/978-3-642-12538-6_8
• Keywords: hyperheuristics, cooperative heuristics, heuristics metaheuristics
• Public URL: https://nottingham-repository.worktribe.com/output/1012064
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-642-12538-6_8
• Additional Information: Presented at the 4th International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Granada, Spain, May 2010.
• Contract Date: Jul 31, 2016