@article {Hemert2006a,
title = {Evolving combinatorial problem instances that are difficult to solve},
journal = {Evolutionary Computation},
volume = {14},
number = {4},
year = {2006},
pages = {433--462},
type = {article},
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 = {constraint programming, constraint satisfaction, evolutionary computation, problem evolving, satisfiability, travelling salesman},
url = {http://www.mitpressjournals.org/toc/evco/14/4},
author = {van Hemert, J. I.}
}
@conference {HS2004,
title = {A Study into Ant Colony Optimization, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems},
booktitle = {Springer Lecture Notes on Computer Science},
number = {3004},
year = {2004},
pages = {114--123},
publisher = {Springer-Verlag, Berlin},
organization = {Springer-Verlag, Berlin},
type = {inproceedings},
abstract = {We compare two heuristic approaches, evolutionary computation and ant colony optimisation, and a complete tree-search approach, constraint programming, for solving binary constraint satisfaction problems. We experimentally show that, if evolutionary computation is far from being able to compete with the two other approaches, ant colony optimisation nearly always succeeds in finding a solution, so that it can actually compete with constraint programming. The resampling ratio is used to provide insight into heuristic algorithms performances. Regarding efficiency, we show that if constraint programming is the fastest when instances have a low number of variables, ant colony optimisation becomes faster when increasing the number of variables.},
keywords = {ant colony optimisation, constraint programming, constraint satisfaction, evolutionary computation},
isbn = {3-540-21367-8},
author = {van Hemert, J. I. and Solnon, C.},
editor = {J. Gottlieb and G. Raidl}
}