[13] | Data Integration in eHealth: A Domain/Disease Specific Roadmap (J Ure, R Proctor, M Martone, D Porteous, S Lloyd, S Lawrie, D Job, R Baldock, A Philp, D Liewald, F Rakebrand, A Blaikie, C McKay, S Anderson, J Ainsworth, J van Hemert, I Blanquer, R Sinnott, C Barillot, F Bernard Gibaud, A Williams, M Hartswood, P Watson, L Smith, A Burger, J Kennedy, H Gonzalez-Velez, R Stevens, O Coecho, R Morton, P Linksted, M Deschenne, M McGilchrist, P Johnson, A Voss, R Gertz, J Wardlaw), In Studies in Health Technology and Informatics (N Jacq, Y Legré, H Muller, I Blanquer, V Breton, D Hausser, V Hernández, T Solomonides, M Hofman-Apitius, eds.), IOPress, volume 126, 2007.
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[12] | Binary Merge Model Representation of the Graph Colouring Problem (I Juhos, A Tóth, J van Hemert), In Evol Comput in Comb Optim (J Gottlieb, G Raidl, eds.), Springer, 2004.
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[11] | A new permutation model for solving the graph k-coloring problem (I Juhos, A Tóth, M Tezuka, P Tann, J van Hemert), In Kalmàr Workshop on Logic and Computer Science, 2003.
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[10] | Evolutionary Computation in Constraint Satisfaction and Machine Learning --- An abstract of my PhD. (J van Hemert), In Proceedings of the Brussels Evolutionary Algorithms Day (BEAD-2001) (Anne Defaweux, Bernard Manderick, Tom Lenearts, Johan Parent, Piet van Remortel, eds.), Vrije Universiteit Brussel (VUB), 2001.
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[9] | Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems (J Eggermont, J van Hemert), In Genetic Programming (J Miller, M Tomassini, PL Lanzi, C Ryan, AGB Tettamanzi, WB Langdon, eds.), Springer, 2001.
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[8] | Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming (J Eggermont, J van Hemert), In Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence (A van den Bosch, H Weigand, eds.), 2000.
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[7] | Mondriaan Art by Evolution (J van Hemert, AE Eiben), In Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (E Postma, M Gyssens, eds.), 1999.
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[6] | Comparing genetic programming variants for data classification (J Eggermont, AE Eiben, J van Hemert), In Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (E Postma, M Gyssens, eds.), 1999.
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[5] | A comparison of genetic programming variants for data classification (J Eggermont, AE Eiben, J van Hemert), In Advances in Intelligent Data Analysis (DJ Hand, JN Kok, MR Berthold, eds.), Springer, 1999.
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[4] | Adapting the Fitness Function in GP for Data Mining (J Eggermont, AE Eiben, J van Hemert), In Genetic Programming (R Poli, P Nordin, WB Langdon, TC Fogarty, eds.), Springer, 1999.
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[3] | Population dynamics and emerging features in AEGIS (AE Eiben, D Elia, J van Hemert), In Proceedings of the Genetic and Evolutionary Computation Conference (W Banzhaf, J Daida, AE Eiben, MH Garzon, V Honavar, M Jakiela, RE Smith, eds.), Morgan Kaufmann Publishers, 1999.
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[2] | Extended abstract: Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function (AE Eiben, J van Hemert, E Marchiori, AG Steenbeek), In Proceedings of the Xth Netherlands/Belgium Conference on Artificial Intelligence (NAIC'98) (JA la Poutré, J van den Herik, eds.), 1998.
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[1] | Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function (AE Eiben, J van Hemert, E Marchiori, AG Steenbeek), In Parallel Problem Solving from Nature (AE Eiben, Th. Bäck, M Schoenauer, H-P Schwefel, eds.), Springer, 1998.
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