Dirk Thierens

Dirk Thierens, David E. Goldberg, D. Goldberg, 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2002, PPSN.

AMaLGaM IDEAs in noisy black-box optimization benchmarking

Dirk Thierens, Peter A. N. Bosman, Jörn Grahl, 2009, GECCO.

Advancing continuous IDEAs with mixture distributions and factorization selection metrics

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2001 .

The Linkage Tree Genetic Algorithm

Dirk Thierens, D. Thierens, 2010, PPSN.

Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm

Dirk Thierens, Martin Pelikan, Mark Hauschild, 2011, GECCO '11.

On the modelling of evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 1999 .

Designing genetic algorithms to solve GIS-problems

Dirk Thierens, de Mt Mark Berg, van Sf Dijk, 2001 .

Genetic Weight Optimization of a Feedforward Neural Network Controller

Johan A. K. Suykens, Joos Vandewalle, Dirk Thierens, 1993 .

Benchmarking Parameter-Free AMaLGaM on Functions With and Without Noise

Dirk Thierens, Peter A. N. Bosman, Jörn Grahl, 2013, Evolutionary Computation.

Linkage neighbors, optimal mixing and forced improvements in genetic algorithms

Dirk Thierens, Peter A. N. Bosman, 2012, GECCO '12.

Hierarchical Genetic Algorithms

Dirk Thierens, Edwin D. de Jong, Richard A. Watson, 2004, PPSN.

Recombinative EMCMC algorithms

Dirk Thierens, Madalina M. Drugan, 2005, 2005 IEEE Congress on Evolutionary Computation.

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

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2017, GECCO.

Building a GA from Design Principles for Learning Bayesian Networks

Dirk Thierens, Linda C. van der Gaag, Steven van Dijk, 2003, GECCO.

On the usefulness of linkage processing for solving MAX-SAT

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2013, GECCO '13.

Exploration and Exploitation Bias of Crossover and Path Relinking for Permutation Problems

Dirk Thierens, 2006, PPSN.

On the complexity of hierarchical problem solving

Dirk Thierens, Edwin D. de Jong, Richard A. Watson, 2005, GECCO '05.

Model-based evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, 2013, GECCO '13 Companion.

Genetic and Evolutionary Computation Conference, GECCO 2007, Proceedings, London, England, UK, July 7-11, 2007, Companion Material

Dirk Thierens, 2007, GECCO.

Dimensional Analysis of Allele-Wise Mixing Revisited

Dirk Thierens, 1996, PPSN.

Model-based evolutionary algorithms: GECCO 2020 tutorial

Dirk Thierens, Peter A. N. Bosman, 2020, GECCO Companion.

Multi-objective Optimization with the Naive MIDEA

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2006 .

Niching an estimation-of-distribution algorithm by hierarchical Gaussian mixture learning

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2017, GECCO.

Adaptive Strategies for Operator Allocation

Dirk Thierens, D. Thierens, 2007, Parameter Setting in Evolutionary Algorithms.

Exploiting gradient information in continuous iterated density estimation evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, 2001 .

Toward a Better Understanding of Mixing in Genetic Algorithms

Dirk Thierens, David E. Goldberg, Kalyanmoy Deb, 1993 .

The Naive MIDEA: A Baseline Multi-objective EA

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2005, EMO.

A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2015, GECCO.

Predetermined versus learned linkage models

Dirk Thierens, Peter A. N. Bosman, 2012, GECCO '12.

Investigation of the traveling thief problem

Dirk Thierens, Rogier Hans Wuijts, R. H. Wuijts, 2019, GECCO.

Model-Based Evolutionary Algorithms

Dirk Thierens, Peter A. N. Bosman, 2015, GECCO.

In Search of Optimal Linkage Trees

Dirk Thierens, Peter A. N. Bosman, Roy de Bokx, 2015, GECCO.

Model-based evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, 2014, GECCO.

Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2006, Scalable Optimization via Probabilistic Modeling.

Non-redundant genetic coding of neural networks

Dirk Thierens, D. Thierens, 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

A bivariate probabilistic model-building genetic algorithm for graph bipartitioning

Dirk Thierens, D. Thierens, 2008, GECCO '08.

Learning Probabilistic Models for Enhanced Evolutionary Computation

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2005 .

Path-Guided Mutation for Stochastic Pareto Local Search Algorithms

Dirk Thierens, Madalina M. Drugan, 2010, PPSN.

Population-Based Iterated Local Search: Restricting Neighborhood Search by Crossover

Dirk Thierens, D. Thierens, 2004, GECCO.

Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization pdf

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2018, ArXiv.

Multi-objective mixture-based iterated density estimation evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2001 .

Learning bayesian network structures with GOMEA

Dirk Thierens, Kalia Orphanou, Peter A. N. Bosnian, 2018, GECCO.

Scalability Problems of Simple Genetic Algorithms

Dirk Thierens, D. Thierens, 1999, Evolutionary Computation.

Real-valued evolutionary multi-modal optimization driven by hill-valley clustering

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2018, GECCO.

Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies

Dirk Thierens, Madalina M. Drugan, D. Thierens, 2012, J. Heuristics.

Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift

Dirk Thierens, Peter A. N. Bosman, Jörn Grahl, 2008, PPSN.

Expanding from Discrete Cartesian to Permutation Gene-pool Optimal Mixing Evolutionary Algorithms

Dirk Thierens, Peter A. N. Bosman, Ngoc Hoang Luong, 2016, GECCO.

Optimal mixing evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2011, GECCO '11.

A generator for hierarchical problems

Dirk Thierens, Edwin D. de Jong, Richard A. Watson, 2005, GECCO '05.

On benchmark properties for adaptive operator selection

Dirk Thierens, 2009, GECCO '09.

On the design and analysis of competent GAs

Dirk Thierens, S. F. van Dijk, M. T. de Berg, 2002 .

On The Design of Genetic Algorithms for Geographical Applications

Dirk Thierens, Mark de Berg, Steven van Dijk, 1999, GECCO.

Learning and exploiting mixed variable dependencies with a model-based EA

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2000, PPSN.

Computation in Gene Expression

Dirk Thierens, Hillol Kargupta, 2001, Complex Syst..

Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2002, Int. J. Approx. Reason..

Linkage Information Processing In Distribution Estimation Algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 1999, GECCO.

Scalability and Efficiency of Genetic Algorithms for Geometrical Applications

Dirk Thierens, Mark de Berg, Steven van Dijk, 2000, PPSN.

Robust genetic algorithms for high quality map labeling

Dirk Thierens, S. F. van Dijk, M. T. de Berg, 1998 .

Evolvability Analysis of the Linkage Tree Genetic Algorithm

Dirk Thierens, Peter A. N. Bosman, 2012, PPSN.

A case study of a multiobjective recombinative genetic algorithm with coevolutionary sharing

Dirk Thierens, Henryk F. R. Arciszewski, M. Neef, 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

Geometrical Recombination Operators for Real-Coded Evolutionary MCMCs

Dirk Thierens, Madalina M. Drugan, 2010, Evolutionary Computation.

Exploiting Modularity, Hierarchy, and Repetition in Variable-Length Problems

Dirk Thierens, Edwin D. de Jong, D. Thierens, 2004, GECCO.

Combining Model-Based EAs for Mixed-Integer Problems

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2014, PPSN.

Generalized adaptive pursuit algorithm for genetic pareto local search algorithms

Dirk Thierens, Madalina M. Drugan, 2011, GECCO '11.

Model-based evolutionary algorithms: GECCO 2017 tutorial

Dirk Thierens, Peter A. N. Bosman, 2017, GECCO.

Evolutionary Markov Chain Monte Carlo

Dirk Thierens, Madalina M. Drugan, D. Thierens, 2003, Artificial Evolution.

Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2007, GECCO '07.

On Measures to Build Linkage Trees in LTGA

Dirk Thierens, Peter A. N. Bosman, 2012, PPSN.

Genetic and Evolutionary Computation – GECCO 2004

Riccardo Poli, Dirk Thierens, Gerhard Goos, 2004, Lecture Notes in Computer Science.

An Adaptive Pursuit Strategy for Allocating Operator Probabilities

Dirk Thierens, D. Thierens, 2005, BNAIC.

Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, Ngoc Hoang Luong, 2021, GECCO Companion.

Benchmark generator for TD Mk landscapes

Dirk Thierens, Tobias van Driessel, 2021, GECCO Companion.

A benchmark generator of tree decomposition Mk landscapes

Dirk Thierens, Tobias van Driessel, 2021, GECCO Companion.

Two-Phase Real-Valued Multimodal Optimization with the Hill-Valley Evolutionary Algorithm

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2021 .

Model-based evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, 2021, GECCO Companion.

A reliable ensemble based approach to semi-supervised learning

Dirk Thierens, Sjoerd de Vries, D. Thierens, 2021, Knowl. Based Syst..

Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms pdf

Dirk Thierens, Peter A. N. Bosman, Marco Virgolin, 2021, ArXiv.

Optimization of multi-objective mixed-integer problems with a model-based evolutionary algorithm in a black-box setting

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2021, GECCO Companion.

Defining Modularity , Hierarchy , and Repetition

Dirk Thierens, Edwin D. de Jong, Richard A. Watson, .

As Based On The Normal Kernels Probability Density Function

Dirk Thierens, Peter A.N. Bosman, 2004 .

Genetic Algorithm with Coevolutionary Sharing

Dirk Thierens, Henryk F. R. Arciszewski, Martijn Neef, 1999 .

Random Keys on ICE: Marginal Product Factorized Probability Distributions in Permutation Optimization

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2002 .

Estimating the significant non-linearities in the genome problem-coding

Dirk Thierens, 1999 .

Multi – objective optimization with the naive M ID E A

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2022 .

A Case Study of a Multiobjective Elitist Recombinative Genetic Algorithm with Coevolutionary Sharing

Dirk Thierens, Henryk F. R. Arciszewski, Martijn Neef, 2000 .