TY - JOUR
T1 - Evolving combinatorial problem instances that are difficult to solve
JF - Evolutionary Computation
Y1 - 2006
A1 - van Hemert, J. I.
KW - constraint programming
KW - constraint satisfaction
KW - evolutionary computation
KW - problem evolving
KW - satisfiability
KW - travelling salesman
AB - 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.
VL - 14
UR - http://www.mitpressjournals.org/toc/evco/14/4
ER -
TY - CONF
T1 - A Study into Ant Colony Optimization, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems
T2 - Springer Lecture Notes on Computer Science
Y1 - 2004
A1 - van Hemert, J. I.
A1 - Solnon, C.
ED - J. Gottlieb
ED - G. Raidl
KW - ant colony optimisation
KW - constraint programming
KW - constraint satisfaction
KW - evolutionary computation
AB - 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.
JF - Springer Lecture Notes on Computer Science
PB - Springer-Verlag, Berlin
SN - 3-540-21367-8
ER -