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ORCIDOliver Kramer 0001
Person information
- affiliation: University of Oldenburg, Department of Computing Science
- affiliation: University of Paderborn, Department of Computer Science
Other persons with the same name
- Oliver Kramer 0002 — Technische Hochschule Rosenheim, Rosenheim, Germany
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2020 – today
- 2024
[c131]Jill Baumann, Oliver Kramer:
Evolutionary Multi-objective Optimization of Large Language Model Prompts for Balancing Sentiments. EvoApplications@EvoStar 2024: 212-224- [i13]Jill Baumann, Oliver Kramer:
Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments. CoRR abs/2401.09862 (2024) - 2023
- [c130]Oliver Kramer:
Enhancing Evolution Strategies with Evolution Path Bias. ESANN 2023 - [c129]Oliver Kramer, Jill Baumann:
Wind Power Prediction with ETSformer. ESANN 2023 - 2022
- [c128]Oliver Kramer:
A Fast and Simple Evolution Strategy with Covariance Matrix Estimation. ESANN 2022 - [c127]Tim Cofala, Oliver Kramer:
An evolutionary fragment-based approach to molecular fingerprint reconstruction. GECCO 2022: 1156-1163 - 2021
- [c126]Patrick Burke, Jonas Prellberg, Oliver Kramer:
Evolutionary Deep Multi-Task Learning. ESANN 2021 - [c125]Tim Cofala, Oliver Kramer:
Transformers for Molecular Graph Generation. ESANN 2021 - [c124]Tim Cofala, Thomas Teusch
, Oliver Kramer:
Spatial Generation of Molecules with Transformers. IJCNN 2021: 1-7 - 2020
- [c123]Tim Cofala, Lars Elend, Oliver Kramer:
Tournament Selection Improves Cartesian Genetic Programming for Atari Games. ESANN 2020: 345-350 - [c122]Oliver Kramer:
Learning Step Size Adaptation in Evolution Strategies. ESANN 2020: 435-440 - [c121]Nils Worzyk, Stefan Niewerth, Oliver Kramer:
Adversarials-1 in Speech Recognition: Detection and Defence. ESANN 2020: 619-624 - [c120]Stefan Oehmcke, Thomas Teusch, Thorben Petersen, Thorsten Klüner, Oliver Kramer:
Modeling H2O/Rutile-TiO2(110) Potential Energy Surfaces with Deep Networks. IJCNN 2020: 1-7 - [c119]Jonas Prellberg, Oliver Kramer:
Learned Weight Sharing for Deep Multi-Task Learning by Natural Evolution Strategy and Stochastic Gradient Descent. IJCNN 2020: 1-8 - [c118]Lars Elend, Sebastian A. Tideman, Kerstin Lopatta, Oliver Kramer:
Earnings Prediction with Deep Learning. KI 2020: 267-274 - [c117]Tim Cofala, Lars Elend, Philip Mirbach, Jonas Prellberg, Thomas Teusch, Oliver Kramer:
Evolutionary Multi-objective Design of SARS-CoV-2 Protease Inhibitor Candidates. PPSN (2) 2020: 357-371 - [i12]Jonas Prellberg, Oliver Kramer:
Learned Weight Sharing for Deep Multi-Task Learning by Natural Evolution Strategy and Stochastic Gradient Descent. CoRR abs/2003.10159 (2020) - [i11]Tim Cofala, Lars Elend, Philip Mirbach, Jonas Prellberg, Thomas Teusch, Oliver Kramer:
Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates. CoRR abs/2005.02666 (2020) - [i10]Lars Elend, Sebastian A. Tideman, Kerstin Lopatta, Oliver Kramer:
Earnings Prediction with Deep Learning. CoRR abs/2006.03132 (2020)
2010 – 2019
- 2019
- [c116]Tim Silhan, Stefan Oehmcke, Oliver Kramer:
Evolution of Stacked Autoencoders. CEC 2019: 823-830 - [c115]Almuth Meier, Oliver Kramer:
Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization. ICANN (2) 2019: 409-421 - [c114]Nils Worzyk, Hendrik Kahlen, Oliver Kramer:
Physical Adversarial Attacks by Projecting Perturbations. ICANN (3) 2019: 649-659 - [c113]Lars Elend, Oliver Kramer:
Self-Organizing Maps with Convolutional Layers. WSOM+ 2019: 23-32 - [i9]Jonas Prellberg, Oliver Kramer:
Acute Lymphoblastic Leukemia Classification from Microscopic Images using Convolutional Neural Networks. CoRR abs/1906.09020 (2019) - 2018
- [j23]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Input quality aware convolutional LSTM networks for virtual marine sensors. Neurocomputing 275: 2603-2615 (2018) - [c112]Nils Worzyk, Oliver Kramer:
Properties of adv-1 - Adversarials of Adversarials. ESANN 2018 - [c111]Oliver Kramer:
Evolution of Convolutional Highway Networks. EvoApplications 2018: 395-404 - [c110]Almuth Meier, Oliver Kramer:
Prediction with Recurrent Neural Networks in Evolutionary Dynamic Optimization. EvoApplications 2018: 848-863 - [c109]Almuth Meier, Oliver Kramer:
Recurrent neural network-predictions for PSO in dynamic optimization. GECCO 2018: 29-36 - [c108]Robert Schadek, Oliver Kramer, Oliver E. Theel:
Predicting Read- and Write-Operation Availabilities of Quorum Protocols based on Graph Properties. ICAART (2) 2018: 550-558 - [c107]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Direct Training of Dynamic Observation Noise with UMarineNet. ICANN (1) 2018: 123-133 - [c106]Oliver Kramer:
Dimensionality Reduction with Evolutionary Shephard-Kruskal Embeddings. ICPRAM 2018: 478-481 - [c105]Jonas Prellberg, Oliver Kramer:
Multi-label Classification of Surgical Tools with Convolutional Neural Networks. IJCNN 2018: 1-8 - [c104]Nils Worzyk, Oliver Kramer:
Adversarials -1: Defending by Attacking. IJCNN 2018: 1-8 - [c103]Stefan Oehmcke, Oliver Kramer:
Knowledge Sharing for Population Based Neural Network Training. KI 2018: 258-269 - [c102]Jonas Prellberg, Oliver Kramer:
Limited Evaluation Evolutionary Optimization of Large Neural Networks. KI 2018: 270-283 - [c101]Jonas Prellberg, Oliver Kramer:
Lamarckian Evolution of Convolutional Neural Networks. PPSN (2) 2018: 424-435 - [i8]Jonas Prellberg, Oliver Kramer:
Multi-label Classification of Surgical Tools with Convolutional Neural Networks. CoRR abs/1805.05760 (2018) - [i7]Jonas Prellberg, Oliver Kramer:
Lamarckian Evolution of Convolutional Neural Networks. CoRR abs/1806.08099 (2018) - [i6]Jonas Prellberg, Oliver Kramer:
Limited Evaluation Evolutionary Optimization of Large Neural Networks. CoRR abs/1806.09819 (2018) - 2017
- [b5]Oliver Kramer:
Genetic Algorithm Essentials. Studies in Computational Intelligence 679, Springer 2017, ISBN 978-3-319-52155-8, pp. 3-84 - [j22]Ali Ahrari, Oliver Kramer:
Finite life span for improving the selection scheme in evolution strategies. Soft Comput. 21(2): 501-513 (2017) - [c100]Manish Aggarwal, Justin Heinermann, Stefan Oehmcke, Oliver Kramer:
Preferences-Based Choice Prediction in Evolutionary Multi-objective Optimization. EvoApplications (1) 2017: 715-724 - [c99]Wei Lee Woon, Stefan Oehmcke, Oliver Kramer:
Spatio-Temporal Wind Power Prediction Using Recurrent Neural Networks. ICONIP (5) 2017: 556-563 - [c98]Daniel Lückehe, Stefan Oehmcke, Oliver Kramer:
Manifold learning with iterative dimensionality photo-projection. IJCNN 2017: 2555-2561 - [c97]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Recurrent neural networks and exponential PAA for virtual marine sensors. IJCNN 2017: 4459-4466 - [c96]Oliver Kramer:
Evolving Kernel PCA Pipelines with Evolution Strategies. KI 2017: 170-177 - [c95]Almuth Meier, Oliver Kramer:
An Experimental Study of Dimensionality Reduction Methods. KI 2017: 178-192 - [e2]Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart E. Madnick:
Data Analytics for Renewable Energy Integration - 4th ECML PKDD Workshop, DARE 2016, Riva del Garda, Italy, September 23, 2016, Revised Selected Papers. Lecture Notes in Computer Science 10097, 2017, ISBN 978-3-319-50946-4 [contents] - [e1]Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart E. Madnick:
Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Skopje, Macedonia, September 22, 2017, Revised Selected Papers. Lecture Notes in Computer Science 10691, Springer 2017, ISBN 978-3-319-71642-8 [contents] - [i5]Oliver Kramer:
Evolution of Convolutional Highway Networks. CoRR abs/1709.03247 (2017) - 2016
- [c94]Daniel Lückehe, Markus Wagner, Oliver Kramer:
Constrained evolutionary wind turbine placement with penalty functions. CEC 2016: 4903-4910 - [c93]Abhishek Awasthi, Jörg Lässig, Thomas Weise, Oliver Kramer:
Tackling Common Due Window Problem with a Two-Layered Approach. COCOA 2016: 772-781 - [c92]Oliver Kramer:
Local Fitness Meta-Models with Nearest Neighbor Regression. EvoApplications (2) 2016: 3-10 - [c91]Judith Neugebauer, Oliver Kramer, Michael Sonnenschein:
Improving Cascade Classifier Precision by Instance Selection and Outlier Generation. ICAART (2) 2016: 96-104 - [c90]Judith Neugebauer, Oliver Kramer, Michael Sonnenschein:
Instance Selection and Outlier Generation to Improve the Cascade Classifier Precision. ICAART (Revised Selected Papers) 2016: 151-170 - [c89]Wei Lee Woon, Oliver Kramer:
Enhanced SVR ensembles for wind power prediction. IJCNN 2016: 2743-2748 - [c88]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
kNN ensembles with penalized DTW for multivariate time series imputation. IJCNN 2016: 2774-2781 - [c87]Judith Neugebauer, Jörg Bremer, Christian Hinrichs, Oliver Kramer, Michael Sonnenschein:
Generalized cascade classification model with customized transformation based ensembles. IJCNN 2016: 4056-4063 - [c86]Björn Wolff, Oliver Kramer, Detlev Heinemann:
Selection of Numerical Weather Forecast Features for PV Power Predictions with Random Forests. DARE@PKDD/ECML 2016: 78-91 - [c85]Justin Heinermann, Jörg Lässig, Oliver Kramer:
Evolutionary Multi-objective Ensembles for Wind Power Prediction. DARE@PKDD/ECML 2016: 92-101 - [c84]Oliver Kramer:
Dimensionality Reduction Hybridizations with Multi-dimensional Scaling. WSOM 2016: 155-163 - [p3]Nils André Treiber, Justin Heinermann, Oliver Kramer:
Wind Power Prediction with Machine Learning. Computational Sustainability 2016: 13-29 - [p2]Björn Wolff, Elke Lorenz, Oliver Kramer:
Statistical Learning for Short-Term Photovoltaic Power Predictions. Computational Sustainability 2016: 31-45 - 2015
- [j21]Oliver Kramer:
Cascade Support Vector Machines with Dimensionality Reduction. Appl. Comput. Intell. Soft Comput. 2015: 216132:1-216132:8 (2015) - [j20]Oliver Kramer, Thole Klingenberg, Michael Sonnenschein, Olaf Wilken:
Non-intrusive appliance load monitoring with bagging classifiers. Log. J. IGPL 23(3): 359-368 (2015) - [j19]Oliver Kramer:
Unsupervised nearest neighbor regression for dimensionality reduction. Soft Comput. 19(6): 1647-1661 (2015) - [c83]Justin Heinermann, Oliver Kramer:
On Heterogeneous Machine Learning Ensembles for Wind Power Prediction. AAAI Workshop: Computational Sustainability 2015 - [c82]Nils André Treiber, Oliver Kramer:
Evolutionary feature weighting for wind power prediction with nearest neighbor regression. CEC 2015: 332-337 - [c81]Oliver Kramer, Daniel Lückehe:
Visualization of evolutionary runs with isometric mapping. CEC 2015: 1359-1363 - [c80]Oliver Kramer:
Evolution strategies with Ledoit-Wolf covariance matrix estimation. CEC 2015: 1712-1716 - [c79]Oliver Kramer:
Supervised Manifold Learning with Incremental Stochastic Embeddings. ESANN 2015 - [c78]Nils André Treiber, Stephan Späth, Justin Heinermann, Lueder von Bremen, Oliver Kramer:
Comparison of Numerical Models and Statistical Learning for Wind Speed Prediction. ESANN 2015 - [c77]Daniel Lückehe, Oliver Kramer:
Alternating Optimization of Unsupervised Regression with Evolutionary Embeddings. EvoApplications 2015: 471-480 - [c76]Oliver Kramer:
Hybrid Manifold Clustering with Evolutionary Tuning. EvoApplications 2015: 481-490 - [c75]Stefan Oehmcke, Justin Heinermann, Oliver Kramer:
Analysis of Diversity Methods for Evolutionary Multi-objective Ensemble Classifiers. EvoApplications 2015: 567-578 - [c74]Daniel Lückehe, Markus Wagner, Oliver Kramer:
On Evolutionary Approaches to Wind Turbine Placement with Geo-Constraints. GECCO 2015: 1223-1230 - [c73]Abhishek Awasthi, Jörg Lässig, Oliver Kramer:
Un-restricted Common Due-Date Problem with Controllable Processing Times - Linear Algorithm for a Given Job Sequence. ICEIS (1) 2015: 526-534 - [c72]Jannes Stubbemann, Nils André Treiber, Oliver Kramer:
Resilient Propagation for Multivariate Wind Power Prediction. ICPRAM (2) 2015: 333-337 - [c71]Oliver Kramer:
Dimensionality reduction in continuous evolutionary optimization. IJCNN 2015: 1-4 - [c70]Justin Heinermann, Oliver Kramer:
Short-Term Wind Power Prediction with Combination of Speed and Power Time Series. KI 2015: 100-110 - [c69]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Event Detection in Marine Time Series Data. KI 2015: 279-286 - [c68]Daniel Lückehe, Oliver Kramer, Manfred Weisensee:
Simulated Annealing With Parameter Tuning for Wind Turbine Placement Optimization. LWA 2015: 108-119 - [c67]Judith Neugebauer, Oliver Kramer, Michael Sonnenschein:
Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data. DARE 2015: 76-93 - 2014
- [b4]Oliver Kramer:
A Brief Introduction to Continuous Evolutionary Optimization. SpringerBriefs in Applied Sciences and Technology, Springer 2014, ISBN 978-3-319-03421-8, pp. I-IX, 1-94 - [j18]Christoph Goebel, Hans-Arno Jacobsen, Victor del Razo, Christoph Doblander, José Rivera, Jens P. Ilg, Christoph M. Flath, Hartmut Schmeck, Christof Weinhardt, Daniel Pathmaperuma, Hans-Jürgen Appelrath, Michael Sonnenschein, Sebastian Lehnhoff, Oliver Kramer, Thorsten Staake, Elgar Fleisch, Dirk Neumann, Jens Strüker, Koray Erek, Rüdiger Zarnekow, Holger Ziekow, Jörg Lässig:
Energy Informatics - Current and Future Research Directions. Bus. Inf. Syst. Eng. 6(1): 25-31 (2014) - [j17]Fabian Gieseke, Antti Airola, Tapio Pahikkala, Oliver Kramer:
Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing 123: 23-32 (2014) - [j16]Tapio Pahikkala, Antti Airola, Fabian Gieseke, Oliver Kramer:
On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers. J. Comput. Sci. Technol. 29(1): 90-104 (2014) - [j15]Christoph Goebel, Hans-Arno Jacobsen, Victor del Razo, Christoph Doblander, José Rivera, Jens P. Ilg, Christoph M. Flath, Hartmut Schmeck, Christof Weinhardt, Daniel Pathmaperuma, Hans-Jürgen Appelrath, Michael Sonnenschein, Sebastian Lehnhoff, Oliver Kramer, Thorsten Staake, Elgar Fleisch, Dirk Neumann, Jens Strüker, Koray Erek, Rüdiger Zarnekow, Holger Ziekow, Jörg Lässig:
Energieinformatik - Aktuelle und zukünftige Forschungsschwerpunkte. Wirtschaftsinf. 56(1): 31-39 (2014) - [c66]Abhishek Awasthi, Jörg Lässig, Oliver Kramer, Thomas Weise:
Common Due-Window problem: Polynomial algorithms for a given processing sequence. CIPLS 2014: 32-39 - [c65]Jörg Lässig, Abhishek Awasthi, Oliver Kramer:
Common Due-Date Problem: Linear Algorithm for a Given Job Sequence. CSE 2014: 97-104 - [c64]Nils André Treiber, Oliver Kramer:
Wind Power Prediction with Cross-Correlation Weighted Nearest Neighbors. EnviroInfo 2014: 63-68 - [c63]Daniel Lückehe, Oliver Kramer, Manfred Weisensee:
An Evolutionary Approach to Geo-Planning of Renewable Energies. EnviroInfo 2014: 501-508 - [c62]Daniel Lückehe, Oliver Kramer:
A variable kernel function for hybrid unsupervised kernel regression. GECCO (Companion) 2014: 77-78 - [c61]Oliver Kramer, Nils André Treiber, Michael Sonnenschein:
Wind Power Ramp Event Prediction with Support Vector Machines. HAIS 2014: 37-48 - [c60]Daniel Lückehe, Oliver Kramer:
Leaving Local Optima in Unsupervised Kernel Regression. ICANN 2014: 137-144 - [c59]Justin Heinermann, Oliver Kramer:
Precise Wind Power Prediction with SVM Ensemble Regression. ICANN 2014: 797-804 - [c58]Jendrik Poloczek, Oliver Kramer:
Multi-stage Constraint Surrogate Models for Evolution Strategies. KI 2014: 255-266 - [c57]Nils André Treiber, Oliver Kramer:
Evolutionary Turbine Selection for Wind Power Predictions. KI 2014: 267-272 - [c56]Olaf Wilken, Oliver Kramer, Enno-Edzard Steen, Andreas Hein:
Activity Recognition Using Non-intrusive Appliance Load Monitoring. PECCS 2014: 40-48 - [c55]Oliver Kramer, Fabian Gieseke, Justin Heinermann, Jendrik Poloczek, Nils André Treiber:
A Framework for Data Mining in Wind Power Time Series. DARE 2014: 97-107 - [c54]Jendrik Poloczek, Nils André Treiber, Oliver Kramer:
KNN Regression as Geo-Imputation Method for Spatio-Temporal Wind Data. SOCO-CISIS-ICEUTE 2014: 185-193 - [i4]Abhishek Awasthi, Jörg Lässig, Oliver Kramer:
A Novel Approach to the Common Due-Date Problem on Single and Parallel Machines. CoRR abs/1405.1234 (2014) - 2013
- [b3]Oliver Kramer:
Dimensionality Reduction with Unsupervised Nearest Neighbors. Intelligent Systems Reference Library 51, Springer 2013, ISBN 978-3-642-38651-0, pp. 1-118 - [j14]Oliver Kramer, Fabian Gieseke, Kai Lars Polsterer:
Learning morphological maps of galaxies with unsupervised regression. Expert Syst. Appl. 40(8): 2841-2844 (2013) - [j13]Oliver Kramer, Fabian Gieseke, Benjamin Satzger:
Wind energy prediction and monitoring with neural computation. Neurocomputing 109: 84-93 (2013) - [j12]Benjamin Satzger, Oliver Kramer:
Goal distance estimation for automated planning using neural networks and support vector machines. Nat. Comput. 12(1): 87-100 (2013) - [c53]Oliver Kramer, Uli Schlachter, Valentin Spreckels:
An adaptive penalty function with meta-modeling for constrained problems. IEEE Congress on Evolutionary Computation 2013: 1350-1354 - [c52]Abhishek Awasthi, Oliver Kramer, Jörg Lässig:
Aircraft Landing Problem: An Efficient Algorithm for a Given Landing Sequence. CSE 2013: 20-27 - [c51]Oliver Kramer, Nils André Treiber, Fabian Gieseke:
Support Vector Machines for Wind Energy Prediction in Smart Grids. EnviroInfo 2013: 16-24 - [c50]Fabian Gieseke, Oliver Kramer:
Towards Non-linear Constraint Estimation for Expensive Optimization. EvoApplications 2013: 459-468 - [c49]Oliver Kramer:
On Missing Data Hybridizations for Dimensionality Reduction. Hybrid Metaheuristics 2013: 189-197 - [c48]Oliver Kramer:
Fast Submanifold Learning with Unsupervised Nearest Neighbors. ICANNGA 2013: 317-325 - [c47]Justin Heinermann, Oliver Kramer, Kai Lars Polsterer, Fabian Gieseke:
On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. KI 2013: 86-97 - [c46]Oliver Kramer:
On Mutation Rate Tuning and Control for the (1+1)-EA. KI 2013: 98-105 - [c45]Jendrik Poloczek, Oliver Kramer:
Local SVM Constraint Surrogate Models for Self-adaptive Evolution Strategies. KI 2013: 164-175 - [c44]Abhishek Awasthi, Jörg Lässig, Oliver Kramer:
Common Due-Date Problem: Exact Polynomial Algorithms for a Given Job Sequence. SYNASC 2013: 258-264 - [i3]Abhishek Awasthi, Jörg Lässig, Oliver Kramer:
Common Due-Date Problem: Exact Polynomial Algorithms for a Given Job Sequence. CoRR abs/1311.2879 (2013) - [i2]Abhishek Awasthi, Oliver Kramer, Jörg Lässig:
Aircraft Landing Problem: Efficient Algorithm for a Given Landing Sequence. CoRR abs/1311.2880 (2013) - 2012
- [j11]Oliver Kramer, Fabian Gieseke:
Evolutionary kernel density regression. Expert Syst. Appl. 39(10): 9246-9254 (2012) - [j10]Oliver Kramer, Christian Igel, Günter Rudolph:
Evolutionary kernel machines. Evol. Intell. 5(3): 151-152 (2012) - [j9]Fabian Gieseke, Oliver Kramer, Antti Airola, Tapio Pahikkala:
Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification. Evol. Intell. 5(3): 189-205 (2012) - [c43]Oliver Kramer:
A Particle Swarm Embedding Algorithm for Nonlinear Dimensionality Reduction. ANTS 2012: 1-12 - [c42]Oliver Kramer:
On Evolutionary Approaches to Unsupervised Nearest Neighbor Regression. EvoApplications 2012: 346-355 - [c41]Oliver Kramer, Olaf Wilken, Petra Beenken, Andreas Hein, Andreas Hüwel, Thole Klingenberg, C. Meinecke, T. Raabe, Michael Sonnenschein:
On Ensemble Classifiers for Nonintrusive Appliance Load Monitoring. HAIS (1) 2012: 322-331 - [c40]Oliver Kramer:
On Unsupervised Nearest-neighbor Regression and Robust Loss Functions. ICAART (1) 2012: 164-170 - [c39]Oliver Kramer:
Sorting High-Dimensional Patterns with Unsupervised Nearest Neighbors. ICAART (Revised Selected Papers) 2012: 250-267 - [c38]Tapio Pahikkala, Antti Airola, Fabian Gieseke, Oliver Kramer:
Unsupervised Multi-class Regularized Least-Squares Classification. ICDM 2012: 585-594 - [c37]Fabian Gieseke, Antti Airola, Tapio Pahikkala, Oliver Kramer:
Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines. ICPRAM (1) 2012: 45-54 - [c36]Oliver Kramer:
Unsupervised Nearest Neighbors with Kernels. KI 2012: 97-106 - 2011
- [j8]Oliver Kramer, Holger Danielsiek:
A Clustering-Based Niching Framework for the Approximation of Equivalent Pareto-Subsets. Int. J. Comput. Intell. Appl. 10(3): 295-311 (2011) - [j7]Oliver Kramer:
On Machine Symbol Grounding and Optimization. Int. J. Cogn. Informatics Nat. Intell. 5(3): 73-85 (2011) - [c35]Oliver Kramer, Tobias Hein:
Monitoring of multivariate wind resources with self-organizing maps and slow feature analysis. CIASG 2011: 69-76 - [c34]Oliver Kramer:
Machine Symbol Grounding and Optimization. ICAART (1) 2011: 464-469 - [c33]Oliver Kramer:
Dimensionality Reduction by Unsupervised K-Nearest Neighbor Regression. ICMLA (1) 2011: 275-278 - [c32]Oliver Kramer, Fabian Gieseke:
Analysis of wind energy time series with kernel methods and neural networks. ICNC 2011: 2381-2385 - [c31]Jörg Lässig, Benjamin Satzger, Oliver Kramer:
Self-Stabilization in Hierarchically Structured Energy Markets. ITNG 2011: 803-809 - [c30]Fabian Gieseke, Oliver Kramer, Antti Airola, Tapio Pahikkala:
Speedy Local Search for Semi-Supervised Regularized Least-Squares. KI 2011: 87-98 - [c29]Oliver Kramer, Fabian Gieseke:
Variance Scaling for EDAs Revisited. KI 2011: 169-178 - [c28]Jörg Lässig, Benjamin Satzger, Oliver Kramer:
Hierarchically Structured Energy Markets as Novel Smart Grid Control Approach. KI 2011: 179-190 - [c27]Oliver Kramer, Fabian Gieseke:
Short-Term Wind Energy Forecasting Using Support Vector Regression. SOCO 2011: 271-280 - [p1]Oliver Kramer, David Echeverría Ciaurri, Slawomir Koziel:
Derivative-Free Optimization. Computational Optimization, Methods and Algorithms 2011: 61-83 - [i1]Oliver Kramer:
Unsupervised K-Nearest Neighbor Regression. CoRR abs/1107.3600 (2011) - 2010
- [j6]Oliver Kramer:
A Review of Constraint-Handling Techniques for Evolution Strategies. Appl. Comput. Intell. Soft Comput. 2010: 185063:1-185063:11 (2010) - [j5]Tzung-Pei Hong, Chuan-Kang Ting, Oliver Kramer:
Theory and Applications of Evolutionary Computation. Appl. Comput. Intell. Soft Comput. 2010: 360796:1-360796:2 (2010) - [j4]Oliver Kramer:
Evolutionary self-adaptation: a survey of operators and strategy parameters. Evol. Intell. 3(2): 51-65 (2010) - [j3]Oliver Kramer:
Covariance Matrix Self-Adaptation and Kernel Regression - Perspectives of Evolutionary Optimization in Kernel Machines. Fundam. Informaticae 98(1): 87-106 (2010) - [j2]Oliver Kramer:
Iterated local search with Powell's method: a memetic algorithm for continuous global optimization. Memetic Comput. 2(1): 69-83 (2010) - [c26]Jan Quadflieg, Mike Preuss, Oliver Kramer, Günter Rudolph:
Learning the track and planning ahead in a car racing controller. CIG 2010: 395-402 - [c25]Oliver Kramer, Holger Danielsiek:
DBSCAN-based multi-objective niching to approximate equivalent pareto-subsets. GECCO 2010: 503-510 - [c24]Oliver Kramer, Benjamin Satzger, Jörg Lässig:
Managing Energy in a Virtual Power Plant Using Learning Classifier Systems. GEM 2010: 111-117 - [c23]Marcel Martin, Jonathan Maycock, Florian Paul Schmidt, Oliver Kramer:
Recognition of Manual Actions Using Vector Quantization and Dynamic Time Warping. HAIS (1) 2010: 221-228 - [c22]Oliver Kramer, Benjamin Satzger, Jörg Lässig:
Power Prediction in Smart Grids with Evolutionary Local Kernel Regression. HAIS (1) 2010: 262-269 - [c21]Benjamin Satzger, Oliver Kramer, Jörg Lässig:
Adaptive Heuristic Estimates for Automated Planning Using Regression. IC-AI 2010: 576-581 - [c20]Fabian Gieseke, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomanns, Ralf-Jurgen Dettmar, Oliver Kramer, Jan Vahrenhold:
Detecting Quasars in Large-Scale Astronomical Surveys. ICMLA 2010: 352-357 - [c19]Tobias Hein, Oliver Kramer:
Recognition and Visualization of Music Sequences Using Self-organizing Feature Maps. KI 2010: 160-167 - [c18]Andreas Thom, Oliver Kramer:
Acceleration of DBSCAN-Based Clustering with Reduced Neighborhood Evaluations. KI 2010: 195-202
2000 – 2009
- 2009
- [b2]Oliver Kramer:
Computational Intelligence - Eine Einführung. Informatik im Fokus, Springer 2009, ISBN 978-3-540-79738-8, pp. I-X, 1-158 - [c17]Sven Rahmann, Tobias Marschall, Frank Behler, Oliver Kramer:
Modeling evolutionary fitness for DNA motif discovery. GECCO 2009: 225-232 - [c16]Patrick Koch, Oliver Kramer, Günter Rudolph, Nicola Beume:
On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization. GECCO 2009: 603-610 - [c15]Oliver Kramer:
Fast Blackbox Optimization: Iterated Local Search and the Strategy of Powell. GEM 2009: 159-163 - [c14]Fabian Gieseke, Tapio Pahikkala, Oliver Kramer:
Fast evolutionary maximum margin clustering. ICML 2009: 361-368 - [c13]Oliver Kramer, André Barthelmes, Günter Rudolph:
Surrogate Constraint Functions for CMA Evolution Strategies. KI 2009: 169-176 - [c12]Oliver Kramer, Patrick Koch:
Rake Selection: A Novel Evolutionary Multi-Objective Optimization Algorithm. KI 2009: 177-184 - [c11]Oliver Kramer, Tobias Hein:
Stochastic Feature Selection in Support Vector Machine Based Instrument Recognition. KI 2009: 727-734 - 2008
- [b1]Oliver Kramer:
Self-Adaptive Heuristics for Evolutionary Computation. University of Paderborn, Germany, Studies in Computational Intelligence 147, Springer 2008, ISBN 978-3-540-69280-5, pp. 1-161 [contents] - [c10]Oliver Kramer:
Self-Adaptive Inversion Mutation for Combinatorial Representations. GEM 2008: 3-9 - [c9]Oliver Kramer:
Premature Convergence in Constrained Continuous Search Spaces. PPSN 2008: 62-71 - 2007
- [c8]Oliver Kramer, Stephan Brügger, Dejan Lazovic:
Sex and death: towards biologically inspired heuristics for constraint handling. GECCO 2007: 666-673 - [c7]Oliver Kramer, Bartek Gloger, Andreas Goebels:
An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments. GECCO 2007: 674-681 - [c6]Oliver Kramer, Patrick Koch:
Self-adaptive partially mapped crossover. GECCO 2007: 1523 - 2006
- [j1]Oliver Kramer, Hans-Paul Schwefel:
On three new approaches to handle constraints within evolution strategies. Nat. Comput. 5(4): 363-385 (2006) - [c5]Steffen Priesterjahn, Oliver Kramer, Alexander Weimer, Andreas Goebels:
Evolution of Human-Competitive Agents in Modern Computer Games. IEEE Congress on Evolutionary Computation 2006: 777-784 - [c4]Oliver Kramer, Benno Stein, Jürgen Wall:
AI and Music: Toward a Taxonomy of Problem Classes. ECAI 2006: 695-696 - 2005
- [c3]Oliver Kramer, Chuan-Kang Ting, Hans Kleine Büning:
A new mutation operator for evolution strategies for constrained problems. Congress on Evolutionary Computation 2005: 2600-2606 - [c2]Oliver Kramer, Chuan-Kang Ting, Hans Kleine Büning:
A mutation operator for evolution strategies to handle constrained problems. GECCO 2005: 917-918 - [c1]Steffen Priesterjahn, Oliver Kramer, Alexander Weimer, Andreas Goebels:
Evolution of Reactive Rules in Multi Player Computer Games Based on Imitation. ICNC (2) 2005: 744-755
Coauthor Index
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