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Paper ID: 2686

Evolving combinatorial problem instances that are difficult to solve
van Hemert,J.I. ,

Appeared in: Evolutionary Computation,
Page Numbers:433--462
Publisher:
Year: 2006
ISBN/ISSN:
Contributing Organisation(s):
Field of Science: e-Science

URL: http://www.mitpressjournals.org/toc/evco/14/4

Abstract: In this paper we demonstrate how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances, thereby stress-testing the corresponding algorithms used to solve these instances. The technique is applied in three important domains of combinatorial optimisation, binary constraint satisfaction, Boolean satisfiability, and the travelling salesman problem. Problem instances acquired through this technique are more difficult than ones found in popular benchmarks. We analyse these evolved instances with the aim to explain their difficulty in terms of structural properties, thereby exposing the weaknesses of corresponding algorithms.

Keywords: problem evolving, evolutionary computation, constraint satisfaction, travelling salesman, satisfiability, constraint programming,


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