| Fine-grained parallel genetic algorithms B Manderick, P Spiessens Proceedings of the third international conference on Genetic algorithms, 428-433, 1989 | 518 | 1989 |
| The genetic algorithm and the structure of fitness landscape B Manderick 4th ICGA, 143-150, 1991 | 366 | 1991 |
| Credit card fraud detection using Bayesian and neural networks S Maes, K Tuyls, B Vanschoenwinkel, B Manderick Proceedings of the 1st international naiso congress on neuro fuzzy …, 2002 | 316 | 2002 |
| Evolvable hardware with genetic learning T Higuchi, M Iwata, I Kajitani, H Yamada, B Manderick, Y Hirao, ... 1996 IEEE International Symposium on Circuits and Systems. Circuits and …, 1996 | 231* | 1996 |
| A massively parallel genetic algorithm implementation and first analysis P Spiessens Proceedings of the Fourth International Conference on GENETIC ALGORITHMS, 1991, 1991 | 227 | 1991 |
| PDB file parser and structure class implemented in Python T Hamelryck, B Manderick Bioinformatics 19 (17), 2308-2310, 2003 | 167 | 2003 |
| Parallel problem solving from nature, 2: proceedings of the second conference on parallel problem solving from nature Brussels, Belgium, 28-30 September 1992 R Männer, B Manderick North Holland, 1992 | 142* | 1992 |
| Evolvable hardware and its application to pattern recognition and fault-tolerant systems T Higuchi, M Iwata, I Kajitani, H Iba, Y Hirao, T Furuya, B Manderick Towards evolvable hardware, 118-135, 1996 | 116 | 1996 |
| Scalarized multi-objective reinforcement learning: Novel design techniques K Van Moffaert, MM Drugan, A Nowé 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement …, 2013 | 85 | 2013 |
| Evolvable hardware T Higuchi, H Iba, B Manderick Massively parallel artificial intelligence, 398-421, 1994 | 73 | 1994 |
| Learning causal bayesian networks from observations and experiments: A decision theoretic approach S Meganck, P Leray, B Manderick International Conference on Modeling Decisions for Artificial Intelligence …, 2006 | 71 | 2006 |
| Global Feature Versus Event Models for Folk Song Classification. R Hillewaere, B Manderick, D Conklin ISMIR 2009, 10th, 2009 | 58 | 2009 |
| Robot planning in partially observable continuous domains JM Porta, MTJ Spaan, N Vlassis Proc. Robotics: Science and Systems, 217-224, 2005 | 49 | 2005 |
| Identifiability of causal effects in a multi-agent causal model S Maes, J Reumers, B Manderick IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT …, 2003 | 45 | 2003 |
| An adaptive rule-based classifier for mining big biological data DM Farid, MA Al-Mamun, B Manderick, A Nowe Expert Systems with Applications 64, 305-316, 2016 | 39 | 2016 |
| String Methods for Folk Tune Genre Classification. R Hillewaere, B Manderick, D Conklin ISMIR 2012, 13th, 2012 | 34 | 2012 |
| String Quartet Classification with Monophonic Models. R Hillewaere, B Manderick, D Conklin ISMIR, 537-542, 2010 | 30 | 2010 |
| Appropriate kernel functions for support vector machine learning with sequences of symbolic data B Vanschoenwinkel, B Manderick International Workshop on Deterministic and Statistical Methods in Machine …, 2004 | 29 | 2004 |
| Parallel Problem Solving from Nature, 2, Brussels, 28.-30. September 1992 R Männer, B Manderick Elsevier Science Publishers, Amsterdam. ga: PPSN2, 1992 | 28 | 1992 |
| How to select genetic operators for combinatorial optimization problems by analyzing their fitness landscape B Manderick Computational Intelligence Imitating Life, 170-181, 1994 | 26 | 1994 |
