D. Thierens

发表

Adapted Maximum-Likelihood Gaussian Models for Numerical Optimization with Continuous EDAs

P. Bosman, Jörn Grahl, D. Thierens, 2007 .

Non-redundant genetic coding of neural networks

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

Domino convergence, drift, and the temporal-salience structure of problems

D. Goldberg, D. Thierens, A. G. Pereira, 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

Scalability Problems of Simple Genetic Algorithms

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

Multi-Objective Optimization with Iterated Density Estimation Evolutionary Algorithms using Mixture Models

D. Thierens, 2004 .

The Naive MIDEA: A Baseline Multi-objective EA

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

Continuous iterated density estimation evolutionary algorithms within the IDEA framework

P. Bosman, D. Thierens, 2000 .

Negative log-likelihood and statistical hypothesis testing as the basis of model selection in IDEAs

P. Bosman, D. Thierens, 2000 .

IDEAs based on the normal kernels probability density function

P. Bosman, D. Thierens, 2000 .

An algorithmic framework for density estimation based evolutionary algorithms

P. Bosman, D. Thierens, 1999 .

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

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

Genetic Weight Optimization of a Feedforward Neural Network Controller

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

Convergence Models of Genetic Algorithm Selection Schemes

Dirk Thierens, David E. Goldberg, D. Goldberg, 1994, PPSN.

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

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

An Algorithmi Framework For Density Estimation Based Evolutionary Algorithms

P. Bosman, D. Thierens, peterb s.uu.nl, 1999 .

Elitist recombination: an integrated selection recombination GA

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

Mixing in Genetic Algorithms

Dirk Thierens, David E. Goldberg, D. Goldberg, 1993, ICGA.

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

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

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

P. Bosman, D. Thierens, 2002 .

GECCO 2007: Genetic and Evolutionary Computation Conference

Thomas Stützle, Riccardo Poli, Mauro Birattari, 2007 .

Negative Log – Likelihood And Statistical Hypothesis Testing

P. Bosman, D. Thierens, 2004 .

Adaptive mutation rate control schemes in genetic algorithms

D. Thierens, 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

Transmission function models of finite population genetic algorithms

J. A. Poutré, D. Thierens, J. Kok, 1998 .

Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms pdf

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

Predictive measures for problem representation and genetic operator design

D. Thierens, D. Thierens, 2002 .

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

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

Multi – objective optimization with the naive M ID E A

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

GECCO ’14: Proceedings of the 2014 conference on Genetic and evolutionary computation

Julian Togelius, Christine Zarges, Hitoshi Iba, 2014 .

Toward a Better Understanding of Mixing in Genetic Algorithms

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

A semi-supervised decision support system to facilitate antibiotic stewardship for urinary tract infections

D. Thierens, J. Oosterheert, S. Vries, 2022, Comput. Biol. Medicine.

The Balance between Proximity and Diversity in Multi(cid:150)Objective Evolutionary Algorithms

P. Bosman, D. Thierens, 2022 .

ENHANCED MAPPING: AN EXTENSION OF TOPOLOGICAL MAPPING TO FORM INTERNAL REPRESENTATIONS AND SPATIAL MAPPINGS

D. Thierens, G. Otte, L. Boullart, 1991 .

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

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

On the design and analysis of competent GAs

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

Scalability and Efficiency of Genetic Algorithms for Geometrical Applications

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

Evolutionary Markov Chain Monte Carlo

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

A reliable ensemble based approach to semi-supervised learning

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

Multi-objective Optimization with the Naive MIDEA

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

Multi-objective Optimization with the Naive $$ \mathbb{M} $$ ID $$ \mathbb{E} $$ A

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2006, Towards a New Evolutionary Computation.

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

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

Learning Probabilistic Models for Enhanced Evolutionary Computation

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

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..

Solving multi-structured problems by introducing linkage kernels into GOMEA

P. Bosman, D. Thierens, T. Alderliesten, 2022, GECCO.

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

Dirk Thierens, D. Thierens, 2004, GECCO.

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

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

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).

Genetic Algorithm with Coevolutionary Sharing

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

Robust genetic algorithms for high quality map labeling

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

The balance between proximity and diversity in multiobjective evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2003, IEEE Trans. Evol. Comput..

An Adaptive Pursuit Strategy for Allocating Operator Probabilities

Dirk Thierens, D. Thierens, 2005, BNAIC.

Convergence Time Analysis for the Multi-objective Counting Ones Problem

Dirk Thierens, D. Thierens, 2003, EMO.

On the usefulness of linkage processing for solving MAX-SAT

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

Ôøøúú Ñùøøøøóò Ööøø Óòøöóð × Blockin

D. Thierens, Dirk Thierens, 2002 .

On the complexity of hierarchical problem solving

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

The Linkage Tree Genetic Algorithm

Dirk Thierens, D. Thierens, 2010, PPSN.

Learning the Neighborhood with the Linkage Tree Genetic Algorithm

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2012, LION.

Multi-objective mixture-based iterated density estimation evolutionary algorithms

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

Benchmarking Parameter-Free AMaLGaM on Functions With and Without Noise

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

More concise and robust linkage learning by filtering and combining linkage hierarchies

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

The roles of local search, model building and optimal mixing in evolutionary algorithms from a bbo perspective

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

AMaLGaM IDEAs in noiseless black-box optimization benchmarking

Dirk Thierens, Peter A. N. Bosman, Jörn Grahl, 2009, GECCO '09.

A Skeleton-Based Approach to Learning Bayesian Networks from Data

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

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

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

A Topology Exploiting Genetic Algorithm to Control Dynamic Systems

Dirk Thierens, Leo Vercauteren, D. Thierens, 1990, PPSN.

On the modelling of evolutionary algorithms

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

Linkage Information Processing In Distribution Estimation Algorithms

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

Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies

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

Hierarchical Genetic Algorithms

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

Investigation of the traveling thief problem

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

Using Genetic Algorithms for Solving Hard Problems in GIS

Dirk Thierens, Mark de Berg, Steven van Dijk, 2002, GeoInformatica.

Optimal mixing evolutionary algorithms

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

AMaLGaM IDEAs in noisy black-box optimization benchmarking

P. Bosman, Jörn Grahl, D. Thierens, 2009, GECCO '09.

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.

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

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

Selection Schemes, Elitist Recombination, and Selection Intensity

Dirk Thierens, D. Thierens, 1997, ICGA.

On the Design and Analysis of Competent Selecto-recombinative GAs

Dirk Thierens, Mark de Berg, Steven van Dijk, 2004, Evolutionary Computation.

Building a GA from Design Principles for Learning Bayesian Networks

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

Adaptive Strategies for Operator Allocation

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

A bivariate probabilistic model-building genetic algorithm for graph bipartitioning

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

Advancing continuous IDEAs with mixture distributions and factorization selection metrics

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

Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations

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

Crossing the road to efficient IDEAs for permutation problems

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