@article{Hemert2006a,
	_day = {01},
	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.},
	annote = {SiteSeer: 40. Evolutionary Computation: 1.94 (top 3.27\%) in 2006 ISI: 1.568 in 2005
},
	author = {van Hemert, J},
	date-added = {2008-08-18 12:42:43 +0100},
	date-modified = {2009-07-28 16:34:45 +0100},
	journal = {Evolutionary Computation},
	keywords = {problem evolving; evolutionary computation; constraint satisfaction; travelling salesman},
	notedisabled = {\textsc{SiteSeer ranks it 40th among its computer science journals, making it part of the top 3.27\% journals (2006); ISI impact factor = 3.000 (2008)}},
	number = {4},
	pages = {433--62},
	title = {Evolving combinatorial problem instances that are difficult to solve},
	url = {http://www.mitpressjournals.org/toc/evco/14/4},
	volume = {14},
	year = {2006},
	bdsk-url-1 = {http://www.mitpressjournals.org/toc/evco/14/4}}