%0 Journal Article
%J Evolutionary Computation
%D 2006
%T Evolving combinatorial problem instances that are difficult to solve
%A van Hemert, J. I.
%K constraint programming
%K constraint satisfaction
%K evolutionary computation
%K problem evolving
%K satisfiability
%K travelling salesman
%X 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.
%B Evolutionary Computation
%V 14
%P 433--462
%G eng
%U http://www.mitpressjournals.org/toc/evco/14/4
%9 article