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Conference Paper
M. Gruber, van Hemert, J. I., and Raidl, G. R., Neighborhood Searches for the Bounded Diameter Minimum Spanning Tree Problem Embedded in a VNS, EA, and ACO, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), Seattle, USA, 2006, vol. 2, p. 1187--1194.
J. I. van Hemert and Eiben, A. E., Mondriaan Art by Evolution, in Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99), 1999, p. 291--292.
J. I. van Hemert and Baldock, R. A., Mining spatial gene expression data for association rules, in Lecture Notes in Bioinformatics, 2007, p. 66--76.
J. I. van Hemert and Bäck, T., Measuring the Searched Space to Guide Efficiency: The Principle and Evidence on Constraint Satisfaction, in Springer Lecture Notes on Computer Science, 2002, p. 23--32.
J. I. van Hemert and Baldock, R. A., Matching Spatial Regions with Combinations of Interacting Gene Expression Patterns, in Communications in Computer and Information Science, 2008, p. 347--361.
I. Juhos and van Hemert, J. I., Improving Graph Colouring Algorithms and Heuristics Using a Novel Representation, in Springer Lecture Notes on Computer Science, 2006, p. 123--134.
I. Juhos, Tóth, A., and van Hemert, J. I., Heuristic Colour Assignment Strategies for Merge Models in Graph Colouring, in Springer Lecture Notes on Computer Science, 2005, p. 132--143.
J. I. van Hemert, Van Hoyweghen, C., Lukschandl, E., and Verbeeck, K., A ``Futurist'' approach to dynamic environments, in Proceedings of the Workshops at the Genetic and Evolutionary Computation Conference, Dynamic Optimization Problems, 2001, p. 35--38.
A. E. Eiben, van Hemert, J. I., Marchiori, E., and Steenbeek, A. G., Extended abstract: Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function, in Proceedings of the Xth Netherlands/Belgium Conference on Artificial Intelligence (NAIC'98), 1998, p. 299--301.
J. I. van Hemert, Evolving binary constraint satisfaction problem instances that are difficult to solve, in Proceedings of the IEEE 2003 Congress on Evolutionary Computation, 2003, p. 1267--1273.
A. Defaweux, Lenaerts, T., and van Hemert, J. I., Evolutionary Transitions as a Metaphor for Evolutionary Optimization, in LNAI 3630, 2005, p. 342--352.
J. I. van Hemert, Evolutionary Computation in Constraint Satisfaction and Machine Learning --- An abstract of my PhD., in Proceedings of the Brussels Evolutionary Algorithms Day (BEAD-2001), 2001.
J. I. van Hemert and Jansen, M. L. M., An Engineering Approach to Evolutionary Art, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 2001, p. 177.
J. I. van Hemert and la Poutré, J. A., Dynamic Routing Problems with Fruitful Regions: Models and Evolutionary Computation, in LNCS, Birmingham, UK, 2004, vol. 3242, p. 690--699.
J. I. van Hemert, Constraint Satisfaction Problems and Evolutionary Algorithms: A Reality Check, in Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00), 2000, p. 267--274.
A. Defaweux, Lenaerts, T., van Hemert, J. I., and Parent, J., Complexity Transitions in Evolutionary Algorithms: Evaluating the impact of the initial population, in Proceedings of the Congress on Evolutionary Computation, 2005, p. 196--203.
J. Eggermont, Eiben, A. E., and van Hemert, J. I., A comparison of genetic programming variants for data classification, in Springer Lecture Notes on Computer Science, 1999, p. 281--290.
J. Eggermont, Eiben, A. E., and van Hemert, J. I., Comparing genetic programming variants for data classification, in Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99), 1999, p. 253--254.
J. I. van Hemert, Comparing Classical Methods for Solving Binary Constraint Satisfaction Problems with State of the Art Evolutionary Computation, in Springer Lecture Notes on Computer Science, 2002, p. 81--90.
I. Juhos, Tóth, A., and van Hemert, J. I., Binary Merge Model Representation of the Graph Colouring Problem, in Springer Lecture Notes on Computer Science, 2004, p. 124--134.
J. Eggermont and van Hemert, J. I., Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems, in Springer Lecture Notes on Computer Science, 2001, p. 23--35.
J. Eggermont, Eiben, A. E., and van Hemert, J. I., Adapting the Fitness Function in GP for Data Mining, in Springer Lecture Notes on Computer Science, 1999, p. 195--204.

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