Publicaties
2022
Wetenschappelijke publicaties
de Vries, S., ten Doesschate, T., Totté, J. E. E., Heutz, J. W., Loeffen, Y. G. T., Oosterheert, J. J., Thierens, D., & Boel, E. (2022). A semi-supervised decision support system to facilitate antibiotic stewardship for urinary tract infections. Computers in Biology and Medicine, 146, [105621]. https://doi.org/10.1016/j.compbiomed.2022.105621
2021
Wetenschappelijke publicaties
Driessel, van, T., & Thierens, D. (2021). Benchmark generator for TD Mk landscapes. In F. Chicano (Ed.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1227-1233). Association for Computing Machinery. https://doi.org/10.1145/3449726.3463177
Thierens, D., & Driessel, van, T. (2021). A benchmark generator of tree decomposition Mk landscapes. In F. Chicano, & K. Krawiec (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 229-230). ACM Press. https://doi.org/10.1145/3449726.3459427
Thierens, D., & Bosman, P. A. N. (2021). Model-Based Evolutionary Algorithms: GECCO 2021 Tutorial. In F. Chicano, & K. Krawiec (Eds.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 558–587). Association for Computing Machinery. https://doi.org/10.1145/3449726.3461417
Przewozniczek, M. W., Komarnicki, M. M., Bosman, P. A. N., Thierens, D., Frej, B., & Luong, N. H. (2021). Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms. In F. Chicano, & K. Krawiec (Eds.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1442–1450). Association for Computing Machinery. https://doi.org/10.1145/3449726.3463152
Sadowski, C., Thierens, D., & Bosman, P. A. N. (2021). Optimization of multi-objective mixed-integer problems with a model-based evolutionary algorithm in a black-box setting. In F. Chicano, & K. Krawiec (Eds.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 227–228). Association for Computing Machinery. https://doi.org/10.1145/3449726.3459521
Maree, S. C., Thierens, D., Alderliesten, T., & Bosman, P. A. N. (2021). Two-Phase Real-Valued Multimodal Optimization with the Hill-Valley Evolutionary Algorithm. In M. Preuss, M. G. Epitropakis, X. Li, & J. E. Fieldsend (Eds.), Metaheuristics for Finding Multiple Solutions (pp. 165-189). (Natural Computing Series). Springer Cham. https://doi.org/10.1007/978-3-030-79553-5_8
de Vries, S., & Thierens, D. (2021). A reliable ensemble based approach to semi-supervised learning. Knowledge-Based Systems, 215, [106738]. https://doi.org/10.1016/j.knosys.2021.106738
2020
Wetenschappelijke publicaties
Folkersma, L., & Thierens, D. (2020). The impact of problem features on NSGA-II and MOEA/D performance. In Parallel Problem Solving from Nature: PPSN 2020 Springer.
Thierens, D., & Bosman, P. A. N. (2020). Model-Based Evolutionary Algorithms: GECCO 2020 Tutorial. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 590-619). (GECCO '20). Association for Computing Machinery (ACM). https://doi.org/10.1145/3377929.3389868
2019
Wetenschappelijke publicaties
Thierens, D., & Bosman, P. A. N. (2019). Model-based evolutionary algorithms. 806-836. Paper presented at the Genetic and Evolutionary Computation Conference Companion. https://doi.org/10.1145/3319619.3323386
Wuijts, R. H., & Thierens, D. (2019). Investigation of the traveling thief problem. 329-337. Paper presented at the Genetic and Evolutionary Computation Conference. https://doi.org/10.1145/3321707.3321766
2018
Wetenschappelijke publicaties
Thierens, D., & Bosman, P. A. N. (2018). Model-based evolutionary algorithms: GECCO 2018 tutorial. 553-583. Paper presented at the Genetic and Evolutionary Computation Conference Companion. https://doi.org/10.1145/3205651.3207874
Aalvanger, G. H., Luong, N. H., Bosman, P. A. N., & Thierens, D. (2018). Heuristics in Permutation GOMEA for Solving the Permutation Flowshop Scheduling Problem. In Lecture Notes in Computer Science, vol 11101 (pp. 146-157). [Chapter 12] (Parallel Problem Solving from Nature – PPSN XV; Vol. 11101). Springer. https://doi.org/10.1007/978-3-319-99253-2_12
Orphanou, K., Thierens, D., & Bosman, P. A. N. (2018). Learning bayesian network structures with GOMEA. 1007-1014. Paper presented at the Genetic and Evolutionary Computation Conference. https://doi.org/10.1145/3205455.3205502
Maree, S. C., Alderliesten, T., Thierens, D., & Bosman, P. A. N. (2018). Real-valued evolutionary multi-modal optimization driven by hill-valley clustering. 857-864. Paper presented at the Genetic and Evolutionary Computation Conference. https://doi.org/10.1145/3205455.3205477
Sadowski, K. L., Thierens, D., & Bosman, P. A. N. (2018). GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems. Evolutionary Computation, 26(1), 117-143. https://doi.org/10.1162/evco_a_00206
2017
Wetenschappelijke publicaties
Thierens, D., & Bosman, P. A. N. (2017). Model-based evolutionary algorithms: GECCO 2017 tutorial. 545-575. Paper presented at the Genetic and Evolutionary Computation Conference Companion. https://doi.org/10.1145/3067695.3067717
Sadowski, K. L., Van Der Meer, M. C., Luong, N. H., Alderliesten, T., Thierens, D., Van Der Laarse, R., Niatsetski, Y., Bel, A., & Bosman, P. A. N. (2017). Exploring trade-offs between target coverage, healthy tissue sparing, and the placement of catheters in HDR brachytherapy for prostate cancer using a novel multi-objective model-based mixed-integer evolutionary algorithm. 1224-1231. Paper presented at the Genetic and Evolutionary Computation Conference. https://doi.org/10.1145/3071178.3071311
Maree, S. C., Alderliesten, T., Thierens, D., & Bosman, P. A. N. (2017). Niching an estimation-of-distribution algorithm by hierarchical Gaussian mixture learning. 713-720. Paper presented at the Genetic and Evolutionary Computation Conference. https://doi.org/10.1145/3071178.3071283
2016
Wetenschappelijke publicaties
Thierens, D., & Bosman, P. A. N. (2016). Model-Based Evolutionary Algorithms. 385-412. Paper presented at the 2016. https://doi.org/10.1145/2908961.2926975
den Besten, W., Thierens, D., & Bosman, P. A. N. (2016). The Multiple Insertion Pyramid: A Fast Parameter-Less Population Scheme. In Proceedings of International Conference on Parallel Problem Solving from Nature (Vol. LNCS 9921, pp. 48-58). Springer. https://doi.org/10.1007/978-3-319-45823-6_5
Bosman, P. A. N., Luong, N. H., & Thierens, D. (2016). Expanding from Discrete Cartesian to Permutation Gene-pool Optimal Mixing Evolutionary Algorithms. In Proceedings of International Conference on Genetic and Evolutionary Computation (pp. 637-644). ACM. https://doi.org/10.1145/2908812.2908917
Sadowski, K. L., Bosman, P. A. N., & Thierens, D. (2016). Learning and exploiting mixed variable dependencies with a model-based EA. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 4382-4389). (Proceedings of IEEE Conference on Evolutionary Computation). IEEE Press. https://doi.org/10.1109/CEC.2016.7744347
2015
Wetenschappelijke publicaties
Bokx, R. D., Thierens, D., & Bosman, P. A. N. (2015). In Search of Optimal Linkage Trees. In J. L. J. Laredo, S. Silva, & A. I. Esparcia-Alcázar (Eds.), Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015, Companion Material Proceedings (pp. 1375-1376). ACM. https://doi.org/10.1145/2739482.2756584
Sadowski, K. L., Bosman, P. A. N., & Thierens, D. (2015). A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables. In J. L. J. Laredo, S. Silva, & A. I. Esparcia-Alcázar (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015 (pp. 911-918). ACM. https://doi.org/10.1145/2739480.2754740
Thierens, D., & Bosman, P. A. N. (2015). Model-Based Evolutionary Algorithms. In J. L. J. Laredo, S. Silva, & A. I. Esparcia-Alcázar (Eds.), Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015, Companion Material Proceedings (pp. 93-120). ACM. https://doi.org/10.1145/2739482.2756584
2014
Wetenschappelijke publicaties
Sadowski, K., Thierens, D., & Bosman, P. (2014). Combining Model-Based EAs for Mixed-Integer Problems. In Parallel problem solving from nature - PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014 : proceedings (pp. 342-351). (Lecture Notes in Computer Science; Vol. 8672). Springer. https://doi.org/10.1007/978-3-319-10762-2_34
2013
Wetenschappelijke publicaties
Thierens, D., & Bosman, P. A. N. (2013). Hierarchical problem solving with the linkage tree genetic algorithm. In C. Blum, & E. Alba (Eds.), Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6-10, 2013 (pp. 877-884). ACM.
Bosman, P. A. N., & Thierens, D. (2013). More concise and robust linkage learning by filtering and combining linkage hierarchies. In C. Blum, & E. Alba (Eds.), Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6-10, 2013 (pp. 359-366). ACM.
Thierens, D., & Bosman, P. A. N. (2013). Model-based evolutionary algorithms. In C. Blum, & E. Alba (Eds.), Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6-10, 2013, Companion Material Proceedings (pp. 377-404). ACM.
2012
Wetenschappelijke publicaties
Thierens, D., & Bosman, P. A. N. (2012). Learning the Neighborhood with the Linkage Tree Genetic Algorithm. In Y. Hamadi, & M. Schoenauer (Eds.), Learning and Intelligent Optimization - 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers (pp. 491-496). Springer.
Bosman, P. A. N., & Thierens, D. (2012). Linkage neighbors, optimal mixing and forced improvements in genetic algorithms. In T. Soule, & J. H. Moore (Eds.), Genetic and Evolutionary Computation Conference, GECCO '12, Philadelphia, PA, USA, July 7-11, 2012 (pp. 585-592). ACM.
Drugan, M. M., & Thierens, D. (2012). Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies. Journal of Heuristics, 18(5), 727-766.
Thierens, D., & Bosman, P. A. N. (2012). Evolvability Analysis of the Linkage Tree Genetic Algorithm. In C. A. Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, & M. Pavone (Eds.), Parallel Problem Solving from Nature - PPSN XII - 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part I (pp. 286-295). Springer.
2011
Wetenschappelijke publicaties
Bosman, P. A. N., & Thierens, D. (2011). The roles of local search, model building and optimal mixing in evolutionary algorithms from a bbo perspective. In N. Krasnogor, & P. L. Lanzi (Eds.), GECCO (Companion) (pp. 663-670). ACM.
2010
Wetenschappelijke publicaties
Drugan, M. M., & Thierens, D. (2010). Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms. Evolutionary Intelligence, 3(2), 79-101.
2009
Wetenschappelijke publicaties
Bosman, P. A. N., Grahl, J., & Thierens, D. (2009). AMaLGaM IDEAs in noiseless black-box optimization benchmarking. In A. Auger, H-G. Beyer, N. Hansen, S. Finck, R. Ros, M. Schoenauer, & D. Whitley (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (Companion), Workshop on Black Box Optimization Benchmarking (pp. 2247-2254). ACM Press.
Thierens, D. (2009). On benchmark properties for adaptive operator selection. In G. Ochoa, E. Ozcan, & M. Schoenauer (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (Companion), Workshop on Automated Heuristic Design: Crossing the Chasm for Search Methods (pp. 2217-2218). ACM Press.
2008
Wetenschappelijke publicaties
Bosman, P. A. N., Grahl, J., & Thierens, D. (2008). Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift. In G. Rudolph. (Ed.), Proceedings of the 10th International Conference on Parallel Problem Solving from Nature (PPSN X) (pp. 133-143). Springer.
2007
Wetenschappelijke publicaties
Bosman, P. A. N., & Thierens, D. (2007). Adaptive Variance Scaling in Continuous Multi-Objective Estimation-of-Distribution Algorithms. In D. Thierens (Ed.), Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO2007) (pp. 500-507). ACM.
Bosman, P. A. N., Grahl, J., & Thierens, D. (2007). Adapted Maximum-Likelihood Gaussian Models for Numerical Optimization with Continuous EDAs. CWI, Amsterdam.
Thierens, D. (2007). Adaptive Strategies for Operator Allocation. In F. G. Lobo, C. F. Lima, & Z. Michalewicz (Eds.), Parameter Setting in Evolutionary Algorithms (pp. 77-90). Springer.
2006
Wetenschappelijke publicaties
Thierens, D. (2006). Exploration and Exploitation Bias of Crossover and Path Relinking for Permutation Problems. In H-G. Beyer, L. D. Whitley, E. K. Burke, T. P. Runarsson, X. Yao, & J. J. Merelo Guervós (Eds.), Parallel Problem Solving from Nature - PPSN IX, Reykjavik, Iceland (pp. 1028-1037). Springer.
2005
Wetenschappelijke publicaties
Thierens, D. (2005). An Adaptive Pursuit Strategy for Allocating Operator Probabilities. In E. A. Beyer (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005) (pp. 1539-1546). ACM Press.
Bosman, P. A. N., & Thierens, D. (2005). The Naive MIDEA: A Baseline Multi-objective EA. In C. A. et al Coello Coello (Ed.), Proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization (EMO 2005) (pp. 428-442). Springer.
Drugan, M. M., & Thierens, D. (2005). Recombinative EMCMC algorithms. In Proceedings of the International Congress on Evolutionary Computation (CEC 2005) (pp. 2024-2031). IEEE Press.
2004
Wetenschappelijke publicaties
Bosman, P. A. N., & Thierens, D. (2004). Learning Probabilistic Models for Enhanced Evolutionary Computation. In Y. Jin (Ed.), Knowledge Incorporation in Evolutionary Computation (pp. 147-176). Springer.
Deb, K., Poli, R., Banzhaf, W., Beyer, H-G., Burke, E., Darwen, P., Dasgupta, D., Floreano, D., Foster, J., Harman, M., Holland, O., Lanzi, P. L., Spector, L., Tettamanzi, A., Thierens, D., & Tyrrell, A. (2004). Genetic and Evolutionary Computation - GECCO 2004 Part II. Springer.
de Jong, E. D., & Thierens, D. (2004). Exploiting Modularity, Hierarchy, and Repetition in Variable-Length Problems. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-04 (pp. 1030-1040)
Pelikan, M., Sastry, K., & Thierens, D. (2004). Optimization by Building and Using Probabilistic Models - OBUPM Workshop. Unknown Publisher.
de Jong, E. D., Thierens, D., & Watson, R. A. (2004). Defining Modularity, Hierarchy, and Repetition. In GECCO 2004 Workshop Proceedings (pp. 2-6). http://www.cs.bham.ac.uk/ wbl/biblio/gecco2004/WMOD003.pdf
de Jong, E. D., Thierens, D., & Watson, R. A. (2004). Hierarchical Genetic Algorithms. In Parallel Problem Solving from Nature - PPSN VIII (pp. 232-241). Springer.
2003
Wetenschappelijke publicaties
Thierens, D. (2003). Convergence Time Analysis for the Multi-objective Counting Ones Problem. (UU-CS ed.) Utrecht University: Information and Computing Sciences.
https://dspace.library.uu.nl/bitstream/handle/1874/23999/thierens_03_convergencetime.pdf?sequence=2
https://dspace.library.uu.nl/bitstream/handle/1874/23999/thierens_03_convergencetime.pdf?sequence=2
Drugan, M. M., & Thierens, D. (2003). Evolutionary Markov chain Monte Carlo. (UU-CS ed.) Utrecht University: Information and Computing Sciences.
https://dspace.library.uu.nl/bitstream/handle/1874/24000/drugan_03_evolutionarymarkov.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/24000/drugan_03_evolutionarymarkov.pdf?sequence=1
2002
Wetenschappelijke publicaties
Bosman, P. A. N., & Thierens, D. (2002). A Thorough Documentation of Obtained Results on Real-Valued Continious and Combinatorial Multi-Objective Optimization Problems Using Diversity Preserving Mixture-Based Iterated Density Estimation Evolutionary Algorithms. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2002-052.html
Bosman, P. A. N., & Thierens, D. (2002). Random Keys on ICE: Marginal Product Factorized Probability Distributions in Permutation Optimization. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2002-054.html
Thierens, D. (2002). Adaptive mutation rate control schemes in genetic algorithms. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2002-056.html
van Dijk, S. F., Thierens, D., & de Berg, M. T. (2002). On the design and analysis of competent GAs. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2002-015.html
Bosman, P. A. N., & Thierens, D. (2002). Permutation optimization by iterated estimation of random keys marginal product factorizations. In J. J. Merelo, P. Adamidis, H. Beyer, J-J. Fernandez-Villicanas, & H-P. Schwefel (Eds.), Parallel Problem Solving from Nature - PPSN VII (pp. 331-340). Springer.
Thierens, D. (2002). Predictive measures for problem representation and genetic operator design. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2002-055.html
van Dijk, S. F., Thierens, D., & de Berg, M. T. (2002). Using genetic algorithms for solving hard problems in GIS. GeoInformatica, 6(4), 381-413.
2001
Wetenschappelijke publicaties
Bosman, P. A. N., & Thierens, D. (2001). Exploiting gradient information in continuous iterated density estimation evolutionary algorithms. In B. Kröse, M. de Rijke, & G. Schreiber (Eds.), Proceedings of the Thirtheenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 69-76). Universiteit Amsterdam.
Bosman, P. A. N., & Thierens, D. (2001). Advancing continuous IDEAs with mixture distributions and factorization selection metrics. In M. Pelikan, & K. Sastry (Eds.), Proceedings of the Optimization by Building and Using Probabilistics Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference GECCO-2001 (pp. 208-212). Morgan Kaufmann.
Bosman, P. A. N., & Thierens, D. (2001). Crossing the road to efficient IDEAs for permutation problems. In L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, H. M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. H. Garzon, & E. Burke (Eds.), Proceedings of the 2001 Genetic and Evolutionary Computation Conference (pp. 219-226). Morgan Kaufmann.
Thierens, D., & Bosman, P. A. N. (2001). Multi-objective optimization with iterated density estimation evolutionary algorithms using mixture models. In A. Ochoa, H. Muehlenbein, T. English, & P. Larranaga (Eds.), Proceedings of the Third International Symposium on Adaptive Systems ISAS-01: Evolutionary Computation and Probabilistic Graphical Models (pp. 129-136)
2000
Wetenschappelijke publicaties
Bosman, P. A. N., & Thierens, D. (2000). Continuous iterated density estimation evolutionary algorithms within the IDEA framework. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2000-15.html
Bosman, P. A. N., & Thierens, D. (2000). Expanding from discrete to continuous estimation of distribution algorithms: The IDEA. In M. Deb Schoenauer, G. Yao Rudolph, E. Merelo Lutton, & H-P. Schwefel (Eds.), Proceedings of the Sixth International Conference on Parallel Problem Solving From Nature - PPSN VI (pp. 767-776). Springer.
Bosman, P. A. N., & Thierens, D. (2000). Mixed IDEAs. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2000-45.html
Bosman, P. A. N., & Thierens, D. (2000). Negative log-likelihood and statistical hypothesis testing as the basis of model selection in IDEAs. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2000-36.html
Bosman, P. A. N., & Thierens, D. (2000). IDEAs based on the normal kernels probability density function. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-2000-11.html
van Dijk, S. F., Thierens, D., & de Berg, M. T. (2000). Scalability and Efficiency of Genetic Algorithms for Geometrical Applications. In M. Deb Schoenauer, R. Yao Günter, E. Merelo Lutton, & H. P. Schwefel (Eds.), Lecture Notes in Computer Science 1917: Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (pp. 683-692). Springer.
Bosman, P. A. N., & Thierens, D. (2000). Continuous iterated density estimation evolutionary algorithms within the IDEA framework. In M. Muehlenbein Pelikan, & A. O. Rodriguez (Eds.), Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference GECCO-2000 (pp. 197-200). Morgan Kauffmann.
Bosman, P. A. N., & Thierens, D. (2000). Negative log-likelihood and statistical hypothesis testing as the basis of model selection in IDEAs. In A. Feelders (Ed.), Proceedings of the Tenth Belgium-Netherlands Conference on Machine Learning (pp. 109-116). Tilburg University.
1999
Wetenschappelijke publicaties
Bosman, P. A. N., & Thierens, D. (1999). On the modelling of evolutionary algorithms. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-1999-11.html
Bosman, P. A. N., & Thierens, D. (1999). On the modelling of evolutionary algorithms. In Proceedings of the Eleventh Belgium-Netherlands Conference on Artificial Intelligence (pp. 67-74). Maastricht University.
Thierens, D. (1999). Estimating the Significant Non-Linearities in the Genome Problem Coding. In W. Banzhaf et al. (Ed.), Proceedings of the 1999 Genetic and Evolutionary Computation Conference (pp. 643-648). Morgan Kaufmann.
Bosman, P. A. N., & Thierens, D. (1999). Linkage information processing in distribution estimation algorithms. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, & R. E. Smith (Eds.), Proceedings of the 1999 Genetic and Evolutionary Computation Conference (pp. 60-67). Morgan Kaufmann Publishers.
van Dijk, S. F., Thierens, D., & de Berg, M. T. (1999). On the Design of Genetic Algorithms for Geographical Applications. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, & R. E. Smith (Eds.), GECCO'99 Proceedings of the Genetic and Evolutionary Computation Conference (pp. 188-195). Morgan Kaufmann.
Neef, M., Thierens, D., & Arciszweski, H. (1999). A case study of a multiobjective elitist recombinative genetic algorithm with coevolutionary sharing. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-1999-49.html
1998
Wetenschappelijke publicaties
Thierens, D. (1998). Dimensional analysis of Allele-Wise mixing revisited. (UU-CS ed.) Utrecht University: Information and Computing Sciences. http://www.cs.uu.nl/research/techreps/UU-CS-1998-47.html
1996
Wetenschappelijke publicaties
Thierens, D. (1996). Non-redundant genetic coding of neural networks. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation-ICE'96 (pp. 571-575). IEEE Press.