Paper Info
Title
Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics
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.
Year
DOI
Venue
2010
10.1007/978-3-642-12538-6_8
Studies in Computational Intelligence
Keywords
Field
DocType
proof of concept,heuristic search
Heuristic,Genetic programming,Travelling salesman problem,Heuristics,Artificial intelligence,Generality,Machine learning,Mathematics
Conference
Volume
ISSN
Citations 
284
1860-949X
1
PageRank 
References 
Authors
0.36
11
4
Name
Order
Citations
PageRank
Germán Terrazas1657.64
Dario Landa Silva231628.38
Natalio Krasnogor3121385.53
Dario Landa-Silva410.36