| [1] | Gagarine Yaikhom, Chee Sun Liew, Liangxiu Han, Jano van Hemert, Malcolm Atkinson, and Amy Krause. Federated enactment of workflow patterns. In P. D'Ambra, M. Guarracina, and D. Talia., editors, Euro-Par, volume LNCS 6271, pages 317-328. Springer, 2010. [ bib ] |
| [2] | K. A. Smith-Miles, J.I. van Hemert, and Y. Lim. Understanding tsp dfficulty by learning from evolved instances. In C. Blum, editor, Proceedings of Learning and Intelligent Optimization, Lecture Notes in Computer Science, page In press. Lecture Notes in Computer Science, 2010. [ bib ] |
| [3] | C.A. Morrison, N. Robertson, A. Turner, J. van Hemert, and J. Koetsier. Molecular orbital calculations of inorganic compounds. In Inorganic Experiments, pages 261-267. Wiley-VCH, 2010. [ bib ] |
| [4] | Robert Kitchen, Vicky Sabine, Andrew Sims, E Jane Macaskill, Lorna Renshaw, Jeremy Thomas, Jano van Hemert, J Michael Dixon, and John Bartlett. Correcting for intra-experiment variation in illumina beadchip data is necessary to generate robust gene-expression profiles. BMC Genomics, 11(1):134, 2010. [ bib | DOI | http ] |
| [5] | D. Rodríguez González, T. Carpenter, J.I. van Hemert, and J. Wardlaw. An open source toolkit for medical imaging de-identification. European Radiology, 20(8):1896-1904, 2010. [ bib | http ] |
| [6] | J. Koetsier and J.I. van Hemert. Rapid development of computational science portals. In S. Gesing and J.I. van Hemert, editors, Proceedings of the IWPLS09 International Workshop on Portals for Life Sciences, CEUR Workshop Proceedings, Edinburgh, September 2009. [ bib | .pdf ] |
| [7] | J.D. Armstrong and J.I. van Hemert. Towards a virtual fly brain. Philosophical Transactions A, 367(1896):2387-2397, June 2009. [ bib | DOI | http | http ] |
| [8] | S. Gesing, O. Kohlbacher, and J.I. van Hemert. Portals for life sciences-a brief introduction. In S. Gesing and J.I. van Hemert, editors, Proceedings of the IWPLS09 International Workshop on Portals for Life Sciences, CEUR Workshop Proceedings, 2009. [ bib | .pdf ] |
| [9] | S. Gesing and J.I. van Hemert, editors. Proceedings of the IWPLS09 International Workshop on Portals for Life Sciences, Edinburgh, UK, 2009. CEUR Workshop Proceedings. [ bib | http ] |
| [10] | L. De Ferrari, S. Aitken, J.I. van Hemert, and I. Goryanin. A model of social collaboration in molecular biology knowledge bases. In Proceedings of the 6th Conference of the European Social Simulation Association (ESSA'09), page In press. European Social Simulation Association, ACM, 2009. [ bib ] |
| [11] | J. Koetsier, A. Turner, P. Richardson, and J.I. van Hemert. Rapid chemistry portals through engaging researchers. In A Trefethen and D De Roure, editors, Fifth IEEE International Conference on e-Science, pages 284-291, 2009. [ bib ] |
| [12] | J.I. van Hemert and J. Koetsier. Giving computational science a friendly face. Zero-In, 1(3):12-13, 2009. [ bib | http ] |
| [13] | J. Fernández, L. Han, A. Nuñez, J. Carretero, and J.I. van Hemert. Using architectural simulation models to aid the design of data intensive application. In Third International Conference on Advanced Engineering Computing and Applications in Sciences, pages 163-168. IEEE Computer Society, 2009. [ bib ] |
| [14] | J. O'Donoghue and J.I. van Hemert. Using the DCC Lifecycle Model to curate a gene expression database: A case study. International Journal of Digital Curation, 4(3), 2009. [ bib ] |
| [15] | A. Barker, J. Weissman, and J.I. van Hemert. The circulate architecture: Avoiding workflow bottlenecks caused by centralised orchestration. Cluster Computing, 12(2):221-235, 2009. [ bib | http ] |
| [16] | T. Lenaerts, A. Defaweux, and J.I. van Hemert. The evolutionary transition algorithm: Evolving complex solutions out of simpler ones. In Raymond Chiong, editor, Nature-Inspired Algorithms for Optimisation, volume 193 of Studies in Computational Intelligence, pages 103-131. Springer, 2009. [ bib ] |
| [17] | J.I. van Hemert and J.A. la Poutré. Exploiting fruitful regions in dynamic routing using evolutionary computation. In F. Pereira Babtista and J. Tavares, editors, Bio-inspired Approaches for the Vehicle Routing Problem, volume 161 of Studies in Computational Intelligence, pages 131-149. Springer, 2009. [ bib ] |
| [18] | L. Han, J.I. van Hemert, R.A. Baldock, and M.P. Atkinson. Automating gene expression annotation for mouse embryo. In Advanced Data Mining and Applications, 5th International Conference, volume 5678 of LNCS, pages 469-478. Springer, 2009. [ bib | DOI ] |
| [19] | A. Barker, J.I. van Hemert, R.A. Baldock, and M.P. Atkinson. An e-infrastructure to support collaborative embryo research. In Cluster Computing and the Grid, pages 520-525. IEEE Computer Society, 2009. [ bib | DOI ] |
| [20] | M.P. Atkinson, J.I. van Hemert, L. Han, A. Hume, and C.S. Liew. A distributed architecture for data mining and integration. In DADC '09: Proceedings of the second international workshop on Data-aware distributed computing, pages 11-20, New York, NY, USA, 2009. ACM. [ bib | DOI ] |
| [21] | A. Barker, J.B. Weissman, and J.I. van Hemert. Eliminating the Middle Man: Peer-to-Peer Dataflow. In HPDC '08: Proceedings of the 17th International Symposium on High Performance Distributed Computing, pages 55-64. ACM, June 2008. [ bib ] |
| [22] | A. Barker, J.B. Weissman, and J.I. van Hemert. Orchestrating Data-Centric Workflows. In The 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid), pages 210-217. IEEE Computer Society, May 2008. [ bib ] |
| [23] | C. Cotta and J.I. van Hemert. Recent Advances in Evolutionary Computation for Combinatorial Optimization, volume 153 of Studies in Computational Intelligence. Springer, 2008. [ bib ] |
| [24] | C. Di Chio, M. Giacobini, and J.I. van Hemert, editors. European Graduate Student Workshop on Evolutionary Computation, 2008. [ bib | .pdf ] |
| [25] | J.I. van Hemert and C. Cotta, editors. Evolutionary Computation in Combinatorial Optimization, 8th European Conference, volume LNCS 4972 of Lecture Notes in Computer Science. Springer, 2008. [ bib | http ] |
| [26] | I. Juhos and J.I. van Hemert. Contraction-based heuristics to improve the efficiency of algorithms solving the graph colouring problem. In C. Cotta and J.I. van Hemert, editors, Recent Advances in Evolutionary Computation for Combinatorial Optimization, Studies in Computational Intelligence, pages 167-184. Springer, 2008. [ bib ] |
| [27] | L. Han and J.I. van Hemert. A novel visual discriminator for network traffic patterns. In Proceedings of the International Conference on Advanced Engineering Computing and Applications in Sciences, pages 141-146, 2008. [ bib | DOI ] |
| [28] | A. Barker and J.I. van Hemert. Scientific workflow: A survey and research directions. In Parallel Processing and Applied Mathematics, volume 4967 of LNCS, pages 746-753. Springer, 2008. [ bib | http ] |
| [29] | J.I. van Hemert and R.A. Baldock. Matching spatial regions with combinations of interacting gene expression patterns. In M. Elloumi and et al., editors, Proceedings of the 2nd International Conference on BioInformatics Research and Development, Communications in Computer and Information Science, pages 347-361. Springer, 2008. [ bib ] |
| [30] | I. Juhos and J.I. van Hemert. Graph colouring heuristics guided by higher order graph properties. In Jano van Hemert and Carlos Cotta, editors, Evolutionary Computation in Combinatorial Optimization, volume 4972 of LNCS, pages 97-108. Springer, 2008. [ bib ] |
| [31] | C. Cotta and J.I. van Hemert, editors. Evolutionary Computation in Combinatorial Optimization, 7th European Conference, volume LNCS 4446 of Lecture Notes in Computer Science. Springer, 2007. [ bib | http ] |
| [32] | M. Giacobini and J.I. van Hemert, editors. European Graduate Student Workshop on Evolutionary Computation, 2007. [ bib | .pdf ] |
| [33] | 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, and J. Wardlaw. Data integration in eHealth: A domain/disease specific roadmap. In N. Jacq, Y. Legré, H. Muller, I. Blanquer, V. Breton, D. Hausser, V. Hernández, T. Solomonides, and M. Hofman-Apitius, editors, Studies in Health Technology and Informatics, volume 126, pages 144-153. IOPress, 2007. [ bib | .pdf ] |
| [34] | J.I. van Hemert and R.A. Baldock. Mining spatial gene expression data for association rules. In S. Hochreiter and R. Wagner, editors, Proceedings of the 1st International Conference on BioInformatics Research and Development, Lecture Notes in Bioinformatics, pages 66-76. Springer, 2007. [ bib | http | .pdf ] |
| [35] | I. Juhos and J.I. van Hemert. Increasing the efficiency of graph colouring algorithms with a representation based on vector operations. Journal of Software, 1(2):24-33, 2006. [ bib | .pdf ] |
| [36] | J.I. van Hemert. Evolving combinatorial problem instances that are difficult to solve. Evolutionary Computation, 14(4):433-462, 2006. [ bib | http ] |
| [37] | M. Giacobini and J.I. van Hemert, editors. European Graduate Student Workshop on Evolutionary Computation, 2006. [ bib | .pdf ] |
| [38] | M. Gruber, J.I. van Hemert, and G.R. Raidl. Neighborhood searches for the bounded diameter minimum spanning tree problem embedded in a VNS, EA, and ACO. In Maarten Keijzer et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1187-1194, Seattle, USA, 2006. ACM. [ bib | .pdf ] |
| [39] | I. Juhos and J.I. van Hemert. Improving graph colouring algorithms and heuristics using a novel representation. In J. Gottlieb and G. Raidl, editors, Evolutionary Computation in Combinatorial Optimization, number 3906 in LNCS, pages 123-134. Springer, 2006. [ bib | .pdf ] |
| [40] | M. Keijzer, A. Tettamanzi, P. Collet, J. van Hemert, and M. Tomassini, editors. Genetic Programming, Proceedings of the 8th European Conference, volume 3447 of Lecture Notes in Computer Science. Springer, 2005. [ bib | http ] |
| [41] | A. Defaweux, T. Lenaerts, J.I. van Hemert, and J. Parent. Complexity transitions in evolutionary algorithms: Evaluating the impact of the initial population. In Proceedings of the Congress on Evolutionary Computation, pages 196-203. IEEE Press, 2005. [ bib | .pdf ] |
| [42] | A. Defaweux, T. Lenaerts, and J.I. van Hemert. Evolutionary transitions as a metaphor for evolutionary optimization. In M. Capcarrere, A.A. Freitas, P.J. Bentley, C.G. Johnson, and J. Timmis, editors, Advances in Artificial Life, LNAI 3630, pages 342-352. Springer, 2005. [ bib | .pdf ] |
| [43] | A. Defaweux, T. Lenaerts, J.I. van Hemert, and J. Parent. Transition models as an incremental approach for problem solving in evolutionary algorithms. In H.-G. Beyer et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference, pages 599-606. ACM Press, 2005. [ bib | .pdf ] |
| [44] | J.I. van Hemert. Property analysis of symmetric travelling salesman problem instances acquired through evolution. In G. Raidl and J. Gottlieb, editors, Evolutionary Computation in Combinatorial Optimization, LNCS, pages 122-131. Springer, 2005. [ bib | .pdf ] |
| [45] | I. Juhos, A. Tóth, and J.I. van Hemert. Heuristic colour assignment strategies for merge models in graph colouring. In G. Raidl and J. Gottlieb, editors, Evolutionary Computation in Combinatorial Optimization, LNCS, pages 132-143. Springer, 2005. [ bib | .pdf ] |
| [46] | J.I. van Hemert and T. Bäck. Robust parameter settings for variation operators by measuring the resampling ratio: A study on binary constraint satisfaction problems. Journal of Heuristics, 10(6):629-640, 2004. [ bib ] |
| [47] | J.I. van Hemert and J.A. la Poutré. Dynamic routing problems with fruitful regions: Models and evolutionary computation. In Xin Yao, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervós, John A. Bullinaria, Jonathan Rowe, Peter Tiňo Ata Kabán, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature, volume 3242 of LNCS, pages 690-699, Birmingham, UK, 2004. Springer. [ bib | .pdf ] |
| [48] | J.I. van Hemert and N.B. Urquhart. Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation. In Xin Yao, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervós, John A. Bullinaria, Jonathan Rowe, Peter Tiňo Ata Kabán, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature, volume 3242 of LNCS, pages 150-159, Birmingham, UK, 2004. Springer. [ bib | http | .pdf ] |
| [49] | I. Juhos, A. Tóth, and J.I. van Hemert. Binary merge model representation of the graph colouring problem. In J. Gottlieb and G. Raidl, editors, Evolutionary Computation in Combinatorial Optimization, number 3004 in LNCS, pages 124-134. Springer, 2004. [ bib | .ps.gz | .pdf ] |
| [50] | J.I. van Hemert and C. Solnon. A study into ant colony optimization, evolutionary computation and constraint programming on binary constraint satisfaction problems. In J. Gottlieb and G. Raidl, editors, Evolutionary Computation in Combinatorial Optimization, number 3004 in LNCS, pages 114-123. Springer, 2004. [ bib | .ps.gz | .pdf ] |
| [51] | B.G.W. Craenen, A.E. Eiben, and J.I. van Hemert. Comparing evolutionary algorithms on binary constraint satisfaction problems. IEEE Transactions on Evolutionary Computation, 7(5):424-444, 2003. [ bib | http | .pdf ] |
| [52] | 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, pages 1267-1273. IEEE Press, 2003. [ bib | .ps.gz | .pdf ] |
| [53] | I. Juhos, A. Tóth, M. Tezuka, P. Tann, and J.I. van Hemert. A new permutation model for solving the graph k-coloring problem. In Kalmàr Workshop on Logic and Computer Science, pages 189-199, 2003. [ bib | .pdf ] |
| [54] | J.I. van Hemert. Application of Evolutionary Computation to Constraint Satisfaction and Data Mining. PhD thesis, Leiden University, 2002. [ bib ] |
| [55] | J.I. van Hemert and T. Bäck. Measuring the searched space to guide efficiency: The principle and evidence on constraint satisfaction. In J.J. Merelo, A. Panagiotis, H.-G. Beyer, José-Luis Fernández-Villacañas, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature, number 2439 in LNCS, pages 23-32. Springer, 2002. [ bib | .ps.gz | .pdf ] |
| [56] | R. Grim, M.L.M. Jansen, A. Baan, J.I. van Hemert, and H. de Wolf. Use of evolutionary algorithms for telescope scheduling. In Torben Anderson, editor, Integrated Modeling of Telescopes, volume 4757, pages 51-61. The International Society for Optical Engineering (SPIE), 2002. [ bib | .ps.gz | .pdf ] |
| [57] | J.I. van Hemert. Comparing classical methods for solving binary constraint satisfaction problems with state of the art evolutionary computation. In S. Cagnoni, J. Gottlieb, E. Hart, M. Middendorf, and G. Raidl, editors, Applications of Evolutionary Computing, number 2279 in LNCS, pages 81-90. Springer, 2002. [ bib | .ps.gz | .pdf ] |
| [58] | J.I. van Hemert. Evolutionary computation in constraint satisfaction and machine learning - an abstract of my PhD. In Anne Defaweux, Bernard Manderick, Tom Lenearts, Johan Parent, and Piet van Remortel, editors, Proceedings of the Brussels Evolutionary Algorithms Day (BEAD-2001). Vrije Universiteit Brussel (VUB), 2001. [ bib | .ps.gz | .pdf ] |
| [59] | J.I. van Hemert and M.L.M. Jansen. An engineering approach to evolutionary art. In Lee Spector, Erik D. Goodman, Annie Wu, W.B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, page 177. Morgan Kaufmann Publishers, 2001. [ bib | .ps.gz | .pdf ] |
| [60] | J.I. van Hemert, C. Van Hoyweghen, E. Lukschandl, and K. Verbeeck. A “futurist” approach to dynamic environments. In J. Branke and Th. Bäck, editors, Proceedings of the Workshops at the Genetic and Evolutionary Computation Conference, Dynamic Optimization Problems, pages 35-38. Morgan Kaufmann Publishers, 2001. [ bib | .ps.gz | .pdf ] |
| [61] | J. Eggermont and J.I. van Hemert. Adaptive genetic programming applied to new and existing simple regression problems. In J. Miller, M. Tomassini, P.L. Lanzi, C. Ryan, A.G.B. Tettamanzi, and W.B. Langdon, editors, Genetic Programming, number 2038 in LNCS, pages 23-35. Springer, 2001. [ bib | .ps.gz | .pdf ] |
| [62] | J.I. van Hemert and M.L.M. Jansen. An engineering approach to evolutionary art. Technical Report TR-01-01, Leiden University, 2001. [ bib | .ps.gz | .pdf ] |
| [63] | J.I. van Hemert, C. Van Hoyweghen, E. Lukschandl, and K. Verbeeck. A “futurist” approach to dynamic environments. Technical Report TR-01-02, Leiden University, 2001. [ bib | .ps.gz | .pdf ] |
| [64] | J.I. van Hemert. Constraint satisfaction problems and evolutionary algorithms: A reality check. In A. van den Bosch and H. Weigand, editors, Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence, pages 267-274. BNVKI, Dutch and the Belgian AI Association, 2000. [ bib | .ps.gz | .pdf ] |
| [65] | J. Eggermont and J.I. van Hemert. Stepwise adaptation of weights for symbolic regression with genetic programming. In A. van den Bosch and H. Weigand, editors, Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence, pages 259-266. BNVKI, Dutch and the Belgian AI Association, 2000. [ bib | .ps.gz | .pdf ] |
| [66] | J.I. van Hemert. De creatieve computer. AIgg Kennisgeving, 13(3):10-18, 2000. [ bib | .ps.gz | .pdf ] |
| [67] | A.E. Eiben and J.I. van Hemert. SAW-ing EAs: adapting the fitness function for solving constrained problems. In D. Corne, M. Dorigo, and F. Glover, editors, New ideas in optimization, chapter 26, pages 389-402. McGraw-Hill, London, 1999. [ bib | .ps.gz | .pdf ] |
| [68] | J. Eggermont, A.E. Eiben, and J.I. van Hemert. Adapting the fitness function in GP for data mining. In R. Poli, P. Nordin, W.B. Langdon, and T.C. Fogarty, editors, Genetic Programming, number 1598 in LNCS, pages 195-204. Springer, 1999. [ bib | .ps.gz ] |
| [69] | J. Eggermont, A.E. Eiben, and J.I. van Hemert. A comparison of genetic programming variants for data classification. In D.J. Hand, J.N. Kok, and M.R. Berthold, editors, Advances in Intelligent Data Analysis, number 1642 in LNCS, pages 281-290. Springer, 1999. [ bib | .ps.gz ] |
| [70] | J. Eggermont, A.E. Eiben, and J.I. van Hemert. Comparing genetic programming variants for data classification. In E. Postma and M. Gyssens, editors, Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence, pages 253-254. BNVKI, Dutch and the Belgian AI Association, 1999. [ bib | .ps.gz ] |
| [71] | J.I. van Hemert and A.E. Eiben. Mondriaan art by evolution. In E. Postma and M. Gyssens, editors, Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence, pages 291-292. BNVKI, Dutch and the Belgian AI Association, 1999. [ bib | .ps.gz ] |
| [72] | A.E. Eiben, D. Elia, and J.I. van Hemert. Population dynamics and emerging features in AEGIS. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela, and R.E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, pages 1257-1264. Morgan Kaufmann Publishers, 1999. [ bib | .ps.gz | .pdf ] |
| [73] | A.E. Eiben, J.K. van der Hauw, and J.I. van Hemert. Graph coloring with adaptive evolutionary algorithms. Journal of Heuristics, 4(1):25-46, 1998. [ bib | .ps.gz ] |
| [74] | A.E. Eiben, J.I. van Hemert, E. Marchiori, and A.G. Steenbeek. Extended abstract: Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function. In J.A. la Poutré and J. van den Herik, editors, Proceedings of the Xth Netherlands/Belgium Conference on Artificial Intelligence (NAIC'98), pages 299-301. BNVKI, Dutch and the Belgian AI Association, 1998. [ bib ] |
| [75] | A.E. Eiben, J.I. van Hemert, E. Marchiori, and A.G. Steenbeek. Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function. In A.E. Eiben, Th. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, number 1498 in LNCS, pages 196-205. Springer, 1998. [ bib | .ps.gz ] |
| [76] | J.I. van Hemert. Applying adaptive evolutionary algorithms to hard problems. Master's thesis, Leiden University, 1998. [ bib | .ps.gz ] |
| [77] | A.E. Eiben, D. Elia, and J.I. van Hemert. The effect of communication in artificial life systems. Technical Report TR-98-12, Leiden University, 1998. [ bib ] |
| [78] | J.I. van Hemert and A.E. Eiben. Comparison of the SAW-ing evolutionary algorithm and the grouping genetic algorithm for graph coloring. Technical Report TR-97-14, Leiden University, 1997. [ bib | .ps.gz ] |
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