TY - CHAP T1 - Contraction-Based Heuristics to Improve the Efficiency of Algorithms Solving the Graph Colouring Problem T2 - Studies in Computational Intelligence Y1 - 2008 A1 - Juhos, I. A1 - van Hemert, J. I. ED - Cotta, C. ED - van Hemert, J. I. KW - constraint satisfaction KW - evolutionary computation KW - graph colouring JF - Studies in Computational Intelligence PB - Springer ER - 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 - Neighborhood Searches for the Bounded Diameter Minimum Spanning Tree Problem Embedded in a VNS, EA, and ACO T2 - Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006) Y1 - 2006 A1 - Gruber, M. A1 - van Hemert, J. I. A1 - Raidl, G. R. ED - Maarten Keijzer ED - et al KW - constraint satisfaction KW - evolutionary computation KW - variable neighbourhood search AB - We consider the Bounded Diameter Minimum Spanning Tree problem and describe four neighbourhood searches for it. They are used as local improvement strategies within a variable neighbourhood search (VNS), an evolutionary algorithm (EA) utilising a new encoding of solutions, and an ant colony optimisation (ACO).We compare the performance in terms of effectiveness between these three hybrid methods on a suite f popular benchmark instances, which contains instances too large to solve by current exact methods. Our results show that the EA and the ACO outperform the VNS on almost all used benchmark instances. Furthermore, the ACO yields most of the time better solutions than the EA in long-term runs, whereas the EA dominates when the computation time is strongly restricted. JF - Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006) PB - ACM CY - Seattle, USA VL - 2 ER - TY - CONF T1 - Dynamic Routing Problems with Fruitful Regions: Models and Evolutionary Computation T2 - LNCS Y1 - 2004 A1 - van Hemert, J. I. A1 - la Poutré, J. A. ED - Xin Yao ED - Edmund Burke ED - Jose A. Lozano ED - Jim Smith ED - Juan J. Merelo-Guerv\'os ED - John A. Bullinaria ED - Jonathan Rowe ED - Peter Ti\v{n}o Ata Kab\'an ED - Hans-Paul Schwefel KW - dynamic problems KW - evolutionary computation KW - vehicle routing AB - We introduce the concept of fruitful regions in a dynamic routing context: regions that have a high potential of generating loads to be transported. The objective is to maximise the number of loads transported, while keeping to capacity and time constraints. Loads arrive while the problem is being solved, which makes it a real-time routing problem. The solver is a self-adaptive evolutionary algorithm that ensures feasible solutions at all times. We investigate under what conditions the exploration of fruitful regions improves the effectiveness of the evolutionary algorithm. JF - LNCS PB - Springer-Verlag CY - Birmingham, UK VL - 3242 SN - 3-540-23092-0 ER - TY - CONF T1 - Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation T2 - LNCS Y1 - 2004 A1 - van Hemert, J. I. A1 - Urquhart, N. B. ED - Xin Yao ED - Edmund Burke ED - Jose A. Lozano ED - Jim Smith ED - Juan J. Merelo-Guerv\'os ED - John A. Bullinaria ED - Jonathan Rowe ED - Peter Ti\v{n}o Ata Kab\'an ED - Hans-Paul Schwefel KW - evolutionary computation KW - problem evolving KW - travelling salesman AB - This paper introduces a generator that creates problem instances for the Euclidean symmetric travelling salesman problem. To fit real world problems, we look at maps consisting of clustered nodes. Uniform random sampling methods do not result in maps where the nodes are spread out to form identifiable clusters. To improve upon this, we propose an evolutionary algorithm that uses the layout of nodes on a map as its genotype. By optimising the spread until a set of constraints is satisfied, we are able to produce better clustered maps, in a more robust way. When varying the number of clusters in these maps and, when solving the Euclidean symmetric travelling salesman person using Chained Lin-Kernighan, we observe a phase transition in the form of an easy-hard-easy pattern. JF - LNCS PB - Springer-Verlag CY - Birmingham, UK VL - 3242 SN - 3-540-23092-0 UR - http://www.vanhemert.co.uk/files/clustered-phase-transition-tsp.tar.gz ER - TY - JOUR T1 - Robust parameter settings for variation operators by measuring the resampling ratio: A study on binary constraint satisfaction problems JF - Journal of Heuristics Y1 - 2004 A1 - van Hemert, J. I. A1 - Bäck, T. KW - constraint satisfaction KW - evolutionary computation KW - resampling ratio AB - In this article, we try to provide insight into the consequence of mutation and crossover rates when solving binary constraint satisfaction problems. This insight is based on a measurement of the space searched by an evolutionary algorithm. From data empirically acquired we describe the relation between the success ratio and the searched space. This is achieved using the resampling ratio, which is a measure for the amount of points revisited by a search algorithm. This relation is based on combinations of parameter settings for the variation operators. We then show that the resampling ratio is useful for identifying the quality of parameter settings, and provide a range that corresponds to robust parameter settings. VL - 10 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 -