01287nas a2200193 4500008004100000245007300041210006900114300001300183490000700196520067600203653002700879653002800906653002900934653002100963653001900984653002401003100001601027856005001043 2006 eng d00aEvolving combinatorial problem instances that are difficult to solve0 aEvolving combinatorial problem instances that are difficult to s a433--4620 v143 aIn 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.10aconstraint programming10aconstraint satisfaction10aevolutionary computation10aproblem evolving10asatisfiability10atravelling salesman1 aHemert, J I uhttp://www.mitpressjournals.org/toc/evco/14/4