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.
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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.
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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.
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Bosman, P.A.N. & Thierens, D. (2012). On Measures to Build Linkage Trees in LTGA. 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. 276-285). Springer.
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Thierens, D. & Bosman, P.A.N. (2012). Predetermined versus learned linkage models. In T. Soule & J.H. Moore (Eds.), Genetic and Evolutionary Computation Conference, GECCO '12, Philadelphia, PA, USA, July 7-11, 2012 (pp. 289-296). ACM.
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Drugan, M.M. & Thierens, D. (2012). Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies. Journal of Heuristics, 18(5), 727-766.
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Drugan, M.M. & Thierens, D. Generalized adaptive pursuit algorithm for genetic pareto local search algorithms. In N. Krasnogor & P.L. Lanzi (Eds.), GECCO'11 (pp. 1963-1970).
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Thierens, D. & Bosman, P.A.N. (2011). Optimal mixing evolutionary algorithms. In N. Krasnogor & P.L. Lanzi (Eds.), GECCO (pp. 617-624).
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Pelikan, M., Hauschild, M. & Thierens, D. (2011). Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm. In N. Krasnogor & P.L. Lanzi (Eds.), GECCO (pp. 1005-1012).
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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.
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Drugan, M.M. & Thierens, D. (2010). Geometrical Recombination Operators for Real-Coded Evolutionary MCMCs. Evolutionary computation, 18(2), 157-198.
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Thierens, D. (2010). Linkage tree genetic algorithm: first results. In M. Pelikan & J. Branke (Eds.), Genetic and Evolutionary Computation Conference, GECCO 2010, Workshop Proceedings, Portland, Oregon, USA, July 7-11, 2010, Companion Material (pp. 1953-1958). ACM.
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Drugan, M.M. & Thierens, D. (2010). Path-Guided Mutation for Stochastic Pareto Local Search Algorithms. In R. Schaefer, C. Cotta, J. Kolodziej & G. Rudolph (Eds.), Parallel Problem Solving from Nature - PPSN XI, 11th International Conference, Kraków, Poland, September 11-15, 2010, Proceedings, Part I (pp. 485-495). Springer.
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Drugan, M.M. & Thierens, D. (2010). Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms. Evolutionary Intelligence, 3(2), 79-101.
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Thierens, D. (2010). The Linkage Tree Genetic Algorithm. In R. Schaefer, C. Cotta, J. Kolodziej & G. Rudolph (Eds.), Parallel Problem Solving from Nature - PPSN XI, 11th International Conference, Kraków, Poland, September 11-15, 2010, Proceedings, Part I (pp. 264-273). Springer.
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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.
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Bosman, P.A.N., Grahl, J. & Thierens, D. (2009). AMaLGaM IDEAs in noisy 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. 2351-2358). ACM Press.
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Thierens, D. (2009). Adaptive Operator Selection for Iterated Local Search. In T. Stützle, M. Birattari & H..H. Hoos (Eds.), Proceedings of the Second international Workshop on Engineering Stochastic Local Search Algorithms (pp. 140-144). Springer.
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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.
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Thierens, D. (2008). A bivariate probabilistic model-building genetic algorithm for graph bipartitioning. In Proceedings of the Genetic and Evolutionary Computation Conference, workshop on optimization by building and using probabilistic models (pp. 2089-2092). ACM.
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Bosman, P.A.N., Grahl, J. & Thierens, D. (2008). Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift. In Günter Rudolph. (Ed.), Proceedings of the 10th International Conference on Parallel Problem Solving from Nature (PPSN X) (pp. 133-143). Springer.
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Bosman, P.A.N., Grahl, J. & Thierens, D. (2007). Adapted Maximum-Likelihood Gaussian Models for Numerical Optimization with Continuous EDAs. Amsterdam: CWI, Amsterdam.
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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, Berlin.
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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.
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Thierens, D. (Ed.). (2007). Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO2007). New York: ACM Press, New York.
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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.
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Thierens, D. (2005). An Adaptive Pursuit Strategy for Allocating Operator Probabilities. In et.al Beyer (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005) (pp. 1539-1546). ACM Press.
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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.
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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-Verlag.
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Jong, E.D. de, Thierens, D. & Watson, R. A. (2004). Defining Modularity, Hierarchy, and Repetition. In GECCO 2004 Workshop Proceedings (pp. 2-6). Seattle, Washington, USA.
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Drugan, M.M. & Thierens, D. (2004). Evolutionary Markov Chain Monte Carlo. In P. Liardet (Ed.), Proceedings of the Sixth International Conference on Artificial Evolution - EA 2003 (pp. 63-76). Springer.
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Jong, E.D. de & 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).
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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. (Eds.). (2004). Genetic and Evolutionary Computation - GECCO 2004 Part I. Springer-Verlag.
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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. (Eds.). (2004). Genetic and Evolutionary Computation - GECCO 2004 Part II. Springer-Verlag.
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Jong, E.D. de, Thierens, D. & Watson, R. A. (2004). Hierarchical Genetic Algorithms. In Parallel Problem Solving from Nature - PPSN VIII (pp. 232-241). Birmingham, UK: Springer-Verlag.
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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-Verlag.
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Dijk, S.F. van, Thierens, D. & Berg, M.T. de (2004). On the design and analysis of competent selecto-recombinative GAs. Evolutionary computation, 12, 243-267.
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Dijk, S.F. van & Thierens, D. (2004). On the use of a non-redundant encoding for learning Bayesian networks from data with a GA. In X. Yao (Ed.), Proceedings of 8th International Conference on Parallel Problem Solving from Nature (pp. 141-150). Springer.
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Pelikan, M., Sastry, K. & Thierens, D. (Eds.). (2004). Optimization by Building and Using Probabilistic Models - OBUPM Workshop..
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Thierens, D. (2004). Population-Based Iterated Local Search: Restricting Neighborhood Search by Crossover. In K. Deb. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004) (pp. 234-245). Springer-Verlag.
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Dijk, S.F. van, Gaag, L.C. van der & Thierens, D. (2003). A Skeleton-Based Approach to Learning Bayesian Networks from Data. In Lecture Notes in Computer Science, Volume 2838: Proceedings of the Seventh Conference on Principles and Practice of Knowledge Discovery in Databases (pp. 132-143). Springer.
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Dijk, S.F. van, Thierens, D. & Gaag, L.C. van der (2003). Building a GA from Design Principles for Learning Bayesian Networks. In Erick Cantú-Paz, James.A. Foster, Kalyanmoy Deb, Lawrence Davis, Rajkumar Roy, Una-May O'Reilly, Hans-Georg Beyer, Russell.K. Standish, Graham Kendall, Stewart.W. Wilson, Mark Harman, Joachim Wegener, Dipankar Dasgupta, Mitchell.A Potter, Alan.C. Schultz, Kathryn.A. Dowsland, Natasa Jonoska & Julian.F. Miller (Eds.), Lecture Notes in Computer Science, Volume 2723: Proceedings of the Genetic and Evolutionary Computation Conference (pp. 886-897). Springer-Verlag.
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Thierens, D. (2003). Convergence Time Analysis for the Multi-objective Counting Ones Problem. (UU-CS2003-046 ). Utrecht: Utrecht University: Information and Computing Sciences.
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Thierens, D. (2003). Convergence Time Analysis for the Multi-objective Counting Ones Problem. In Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimization (pp. 355-364). Springer Verlag.
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Drugan, M.M. & Thierens, D. (2003). Evolutionary Markov chain Monte Carlo. (UU-CS2003-047 ). Utrecht: Utrecht University: Information and Computing Sciences.
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Bosman, P.A.N. & Thierens, D. (2003). The Balance between Proximity and Diversity in Multi--Objective Evolutionary Algorithms. IEEE transactions on evolutionary computation, 7(2), 174-188.
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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-CS2002-052 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. (2002). Adaptive mutation rate control schemes in genetic algorithms. In Proceedings of the 2002 IEEE World Congress on Computational Intelligence: Congress on Evolutionary Computation (pp. 980-985). IEEE Press.
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Thierens, D. (2002). Adaptive mutation rate control schemes in genetic algorithms. (UU-CS2002-056 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Drugan, M.M., Thierens, D. & Gaag, L.C. van der (2002). MDL-based feature selection for Bayesian network classifiers. In M. Blockeel & M. Denecker (Eds.), Proceedings of the Fourteenth Belgium-Netherlands Conference on Artificial Intelligence (pp. 99-106). Leuven, Belgium.
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Bosman, P.A.N. & Thierens, D. (2002). Multi--objective optimization with diversity preserving mixture--based iterated density estimation evolutionary algorithms. International Journal of Approximate Reasoning, 31, 259-289.
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Dijk, S.F. van, Thierens, D. & Berg, M.T. de (2002). On the design and analysis of competent GAs. (UU-CS2002-015 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Bosman, P.A.N. & Thierens, D. (2002). Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations. (UU-CS2002-053 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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). Berlin, Germany: Springer-Verlag.
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Thierens, D. (2002). Predictive measures for problem representation and genetic operator design. (UU-CS2002-055 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Bosman, P.A.N. & Thierens, D. (2002). Random Keys on ICE: Marginal Product Factorized Probability Distributions in Permutation Optimization. (UU-CS2002-054 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. (2002). Some issues in predictive measures for representation and genetic operator design. In Proceedings of the Representations for genetic and Evolutionaty Algorithms Workshop, Genetic and Evolutionary Computation Conference (pp. 193-199). Morgan Kaufmann.
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Dijk, S.F. van, Thierens, D. & Berg, M.T. de (2002). Using genetic algorithms for solving hard problems in GIS. GeoInformatica, 6(4), 381-413.
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Bosman, P.A.N. & Thierens, D. (2001). Advancing continuous IDEAs with mixed distributions and factorization selection metrics. (UU-CS2001-51 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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). San Francisco, California, U.S.A.: Morgan Kaufmann.
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Bosman, P.A.N. & Thierens, D. (2001). Crossing the road to efficient IDEAs for permutation problems. (UU-CS2001-50 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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). San Francisco, California, U.S.A.: Morgan Kaufmann.
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Dijk, S.F. van, Thierens, D. & Berg, M.T. de (2001). Designing Genetic Algorithms to Solve GIS Problems. In R. Krzanowski & J. Raper (Eds.), Spatial Evolutionary Modeling (pp. 158-180). New York: Oxford University Press.
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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). Amsterdam, The Netherlands: Universiteit Amsterdam.
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Bosman, P.A.N. & Thierens, D. (2001). Exploiting gradient information in continuous iterated density estimation evolutionary algorithms. (UU-CS2001-53 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. & Bosman, P.A.N. (2001). Multi-objective mixture based iterated density estimation evolutionary algorithms. (UU-CS2001-59 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. & Bosman, P.A.N. (2001). Multi-objective mixture-based iterated density estimation evolutionary algorithms. In L. Spector et al (Ed.), Proceedings of the 2001 Genetic and Evolutionary Computation Conference (pp. 663-670). San Francisco, U.S.A.: Morgan Kaufmann.
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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).
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Bosman, P.A.N. & Thierens, D. (2001). New IDEAs and more ICE by learning and using unconditional permutation factorizations. In D. Whitley (Ed.), Late-Breaking Papers of the Genetic and Evolutionary Computation Conference - GECCO - 2001 (pp. 16-23). San Francisco, California, U.S.A.: Morgan Kaufmann.
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Bosman, P.A.N. & Thierens, D. (2001). New IDEAs and more ICE by learning and using unconditional permutation factorizations. (UU-CS2001-52 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Bosman, P.A.N. & Thierens, D. (2000). Continuous iterated density estimation evolutionary algorithms within the IDEA framework. (UU-CS2000-15 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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). San Francisco, California: Morgan Kauffmann.
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Bosman, P.A.N. & Thierens, D. (2000). Expanding from discrete to continuous estimation of distribution algorithms: The IDEA. (UU-CS2000-26 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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). Berlin: Springer-Verlag.
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Bosman, P.A.N. & Thierens, D. (2000). IDEAs based on the normal kernels probability density function. (UU-CS2000-11 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Bosman, P.A.N. & Thierens, D. (2000). Mixed IDEAs. (UU-CS2000-45 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Bosman, P.A.N. & Thierens, D. (2000). Negative log-likelihood and statistical hypothesis testing as the basis of model selection in IDEAs. (UU-CS2000-36 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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: Tilburg University.
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Dijk, S.F. van, Thierens, D. & Berg, M.T. de (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). Berlijn Heidelberg: Springer-Verlag.
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Thierens, D., Berg, M.T. de & Dijk, S.F. van (2000). Using genetic algorithms for solving hard problems in GIS. (UU-CS2000-32 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Neef, M, Thierens, D. & Arciszewski, H (1999). A Case Study of a Multiobjective Elitist Recombinative Genetic Algorithm with Coevolutionary Sharing. In P Angeline (Ed.), Proceedings of the International Congress on Evolutionary Computation (pp. 796-803). Priscatawy: IEEE Press.
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Neef, M, Thierens, D. & Arciszweski, H. (1999). A case study of a multiobjective elitist recombinative genetic algorithm with coevolutionary sharing. (UU-CS1999-49 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Bosman, P.A.N. & Thierens, D. (1999). An algorithmic framework for density estimation based evolutionary algorithms. (UU-CS1999-46 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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). San Fransisco: Morgan Kaufmann.
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Thierens, D. (1999). Estimating the significant non-linearities in the genome problem-coding. (UU-CS1999-47 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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). San Francisco, CA: Morgan Kaufmann Publishers.
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Bosman, P.A.N. & Thierens, D. (1999). Linkage information processing in distribution estimation algorithms. (UU-CS1999-10 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Dijk, S.F. van, Thierens, D. & Berg, M.T. de (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). San Francisco, CA: Morgan Kaufmann.
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Bosman, P.A.N. & Thierens, D. (1999). On the modelling of evolutionary algorithms. (UU-CS1999-11 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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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: Maastricht University.
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Thierens, D. (1999). On the scalability of simple genetic algorithms. (UU-CS1999-48 ). Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. (1999). Scalability Problems of Simple Genetic Algorithms. Evolutionary computation, 7(4), 331-352.
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Thierens, D. (1998). Dimensional analysis of Allele-Wise mixing revisited. (UU-CS1998-47 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D., Goldberg, D.E.G. & Pereira, A.G. (1998). Domino convergence, drift, and the temporal-salience structure of problems. In Proceedings of the 1998 IEEE World Congress on Computational Intelligence (pp. 535-540). IEEE Press.
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Thierens, D. (1998). Domino convergence, drift, and the temporal-salience structure of problems. (UU-CS1998-49 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. (1998). Non-redundant genetic coding of neural networks. (UU-CS1998-46 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D., Berg, M.T. de & Dijk, S.F. van (1998). Robust genetic algorithms for high quality map labeling. (UU-CS1998-41 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. (1998). Selection schemes, elitist recombination, and selection intensity. (UU-CS1998-48 ). Utrecht, the Netherlands: Utrecht University: Information and Computing Sciences.
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Thierens, D. (1997). Selection schemes, elitist recombination, and selection intensity. In T. Back (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (pp. 152-159). San Fransisco, U.S.A.: Morgan Kaufmann.
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Thierens, D. (1996). Dimensional analysis of allele-wise mixing revisited. In Proceedings of the Fourth International Conference on Parallel Problem Solving from Nature (pp. 255-265). Springer.
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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.
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