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Markus Olhofer
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- affiliation: Honda Research Institute Europe, Offenbach, Germany
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2020 – today
- 2024
- [j24]Mitra Baratchi, Can Wang, Steffen Limmer, Jan N. van Rijn, Holger H. Hoos, Thomas Bäck, Markus Olhofer:
Automated machine learning: past, present and future. Artif. Intell. Rev. 57(5): 122 (2024) - [j23]Xilu Wang
, Yaochu Jin
Alleviating Search Bias in Bayesian Evolutionary Optimization With Many Heterogeneous Objectives. IEEE Trans. Syst. Man Cybern. Syst. 54(1): 143-155 (2024) - [c51]Alistair Benford, Markus Olhofer, Tobias Rodemann, Per Kristian Lehre:
Bicriteria Optimisation of Average and Worst-Case Performance Using Coevolutionary Algorithms. CEC 2024: 1-8 - [c50]Angus Kenny
A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour. EvoApplications@EvoStar 2024: 115-129 - [c49]Angus Kenny
Using Bayesian Optimization to Improve Hyperparameter Search in TPOT. GECCO 2024 - [c48]Christian Internò, Markus Olhofer, Yaochu Jin, Barbara Hammer:
Federated Loss Exploration for Improved Convergence on Non-IID Data. IJCNN 2024: 1-8 - [c47]Ahmed R. Sadik, Sebastian Brulin, Markus Olhofer:
Coding by Design: GPT-4 Empowers Agile Model Driven Development. MODELSWARD 2024: 149-156 - [i7]Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer:
Automated Federated Learning via Informed Pruning. CoRR abs/2405.10271 (2024) - 2023
- [j22]Nivesh Dommaraju
Evaluation of geometric similarity metrics for structural clusters generated using topology optimization. Appl. Intell. 53(1): 904-929 (2023) - [j21]Qiqi Liu
Surrogate-Assisted Many-Objective Optimization of Building Energy Management. IEEE Comput. Intell. Mag. 18(4): 14-28 (2023) - [j20]Xilu Wang
Recent Advances in Bayesian Optimization. ACM Comput. Surv. 55(13s): 287:1-287:36 (2023) - [j19]Guo Yu
Solution Set Augmentation for Knee Identification in Multiobjective Decision Analysis. IEEE Trans. Cybern. 53(4): 2480-2493 (2023) - [c46]Angus Kenny
Hybridizing TPOT with Bayesian Optimization. GECCO 2023: 502-510 - [i6]Sebastian Brulin, Markus Olhofer:
Bi-level Network Design for UAM Vertiport Allocation Using Activity-Based Transport Simulations. CoRR abs/2307.14731 (2023) - [i5]Ahmed R. Sadik, Sebastian Brulin, Markus Olhofer:
Coding by Design: GPT-4 empowers Agile Model Driven Development. CoRR abs/2310.04304 (2023) - 2022
- [j18]Hemant Kumar Singh
Investigating the Use of Linear Programming and Evolutionary Algorithms for Multi-Objective Electric Vehicle Charging Problem. IEEE Access 10: 115322-115337 (2022) - [j17]Tapabrata Ray, Hemant Kumar Singh
Towards identification of solutions of interest for multi-objective problems considering both objective and variable space information. Appl. Soft Comput. 119: 108505 (2022) - [j16]Xilu Wang, Yaochu Jin
Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times. Evol. Comput. 30(2): 221-251 (2022) - [c45]Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck:
Cooperative Multi-objective Topology Optimization Using Clustering and Metamodeling. CEC 2022: 1-8 - [c44]Nikolas Hohmann
Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios. CEC 2022: 1-8 - [i4]Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer:
Recent Advances in Bayesian Optimization. CoRR abs/2206.03301 (2022) - [i3]Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer:
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives. CoRR abs/2208.12217 (2022) - 2021
- [j15]Xilu Wang, Yaochu Jin
Transfer learning based surrogate assisted evolutionary bi-objective optimization for objectives with different evaluation times. Knowl. Based Syst. 227: 107190 (2021) - [j14]Yali Wang, Steffen Limmer, Markus Olhofer
Automatic preference based multi-objective evolutionary algorithm on vehicle fleet maintenance scheduling optimization. Swarm Evol. Comput. 65: 100933 (2021) - [j13]Guo Yu
A Multiobjective Evolutionary Algorithm for Finding Knee Regions Using Two Localized Dominance Relationships. IEEE Trans. Evol. Comput. 25(1): 145-158 (2021) - [c43]Van Duc Nguyen, Ewout Zwanenburg, Steffen Limmer, Wessel Luijben, Thomas Bäck
A Combination of Fourier Transform and Machine Learning for Fault Detection and Diagnosis of Induction Motors. DSA 2021: 344-351 - [c42]Nikolas Hohmann
Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs. SSCI 2021: 1-8 - [i2]Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Bäck:
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization. CoRR abs/2101.09556 (2021) - [i1]Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer:
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times. CoRR abs/2108.13339 (2021) - 2020
- [j12]Xilu Wang
An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization. Inf. Sci. 519: 317-331 (2020) - [j11]Guo Yu
Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization. IEEE Trans. Cybern. 50(8): 3531-3544 (2020) - [j10]David Guirguis
Evolutionary Black-Box Topology Optimization: Challenges and Promises. IEEE Trans. Evol. Comput. 24(4): 613-633 (2020) - [c41]Tapabrata Ray, Hemant Kumar Singh
Online intensification of search around solutions of interest for multi/many-objective optimization. CEC 2020: 1-8 - [c40]Xilu Wang, Yaochu Jin
Transfer learning for gaussian process assisted evolutionary bi-objective optimization for objectives with different evaluation times. GECCO 2020: 587-594
2010 – 2019
- 2019
- [c39]Yali Wang, Steffen Limmer, Markus Olhofer, Michael T. M. Emmerich
Vehicle Fleet Maintenance Scheduling Optimization by Multi-objective Evolutionary Algorithms. CEC 2019: 442-449 - [c38]Guo Yu
References or Preferences - Rethinking Many-objective Evolutionary Optimization. CEC 2019: 2410-2417 - [c37]Guo Yu
An a priori knee identification multi-objective evolutionary algorithm based on α-dominance. GECCO (Companion) 2019: 241-242 - [c36]Marios Kefalas
A tabu search-based memetic algorithm for the multi-objective flexible job shop scheduling problem. GECCO (Companion) 2019: 1254-1262 - [c35]Hemant Kumar Singh
Identifying solutions of interest for practical many-objective problems using recursive expected marginal utility. GECCO (Companion) 2019: 1734-1741 - [c34]Nivesh Dommaraju
Identifying Topological Prototypes using Deep Point Cloud Autoencoder Networks. ICDM Workshops 2019: 761-768 - [c33]Elena Raponi, Mariusz Bujny, Markus Olhofer, Simonetta Boria, Fabian Duddeck:
Hybrid Kriging-assisted Level Set Method for Structural Topology Optimization. IJCCI 2019: 70-81 - [c32]Can Wang, Thomas Bäck
Automated Machine Learning for Short-term Electric Load Forecasting. SSCI 2019: 314-321 - 2018
- [c31]Guo Yu
A Method for a Posteriori Identification of Knee Points Based on Solution Density. CEC 2018: 1-8 - [c30]Sander van Rijn
Multi-fidelity surrogate model approach to optimization. GECCO (Companion) 2018: 225-226 - [c29]Mariusz Bujny, Nikola Aulig, Markus Olhofer, Fabian Duddeck
Learning-based topology variation in evolutionary level set topology optimization. GECCO 2018: 825-832 - [c28]Theodoros Georgiou, Sebastian Schmitt, Markus Olhofer, Yu Liu, Thomas Bäck
Learning Fluid Flows. IJCNN 2018: 1-8 - 2017
- [j9]Ran Cheng
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization. IEEE Trans. Cybern. 47(12): 4108-4121 (2017) - [j8]Ran Cheng
Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation. IEEE Trans. Emerg. Top. Comput. Intell. 1(2): 97-111 (2017) - 2016
- [j7]Kaname Narukawa, Yu Setoguchi, Yuki Tanigaki, Markus Olhofer, Bernhard Sendhoff
Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization. Soft Comput. 20(7): 2733-2757 (2016) - [j6]Ran Cheng
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization. IEEE Trans. Evol. Comput. 20(5): 773-791 (2016) - [c27]Nikola Aulig, Markus Olhofer:
State-based representation for structural topology optimization and application to crashworthiness. CEC 2016: 1642-1649 - [c26]Nikola Aulig, Markus Olhofer:
Evolutionary computation for topology optimization of mechanical structures: An overview of representations. CEC 2016: 1948-1955 - [c25]Mariusz Bujny
Hybrid evolutionary approach for level set topology optimization. CEC 2016: 5092-5099 - 2015
- [c24]Ran Cheng
Reference vector based a posteriori preference articulation for evolutionary multiobjective optimization. CEC 2015: 939-946 - [c23]Nikola Aulig, Markus Olhofer:
Neuro-evolutionary Topology Optimization with Adaptive Improvement Threshold. EvoApplications 2015: 655-666 - 2014
- [c22]Olga Smalikho, Markus Olhofer:
Co-evolution of Sensory System and Signal Processing for Optimal Wing Shape Control. EvoApplications 2014: 805-816 - [c21]Olga Smalikho, Markus Olhofer:
Growth in co-evolution of sensory system and signal processing for optimal wing control. GECCO 2014: 729-736 - [c20]Nikola Aulig, Markus Olhofer:
Neuro-evolutionary topology optimization of structures by utilizing local state features. GECCO 2014: 967-974 - [c19]Olga Smalikho, Markus Olhofer:
Adaptive System Design by a Simultaneous Evolution of Morphology and Information Processing. SEAL 2014: 25-36 - 2013
- [c18]Nikola Aulig, Markus Olhofer:
Evolutionary generation of neural network update signals for the topology optimization of structures. GECCO (Companion) 2013: 213-214 - [c17]Edgar Reehuis, Markus Olhofer, Michael Emmerich, Bernhard Sendhoff
Novelty and interestingness measures for design-space exploration. GECCO 2013: 1541-1548 - [c16]Edgar Reehuis, Markus Olhofer, Bernhard Sendhoff
Learning-Guided Exploration in Airfoil Optimization. IDEAL 2013: 505-512 - 2011
- [j5]Markus Olhofer, Dilyana Yankulova, Bernhard Sendhoff
Autonomous experimental design optimization of a flapping wing. Genet. Program. Evolvable Mach. 12(1): 23-47 (2011) - 2010
- [c15]Lars Gräning, Nikola Aulig, Markus Olhofer:
Towards Directed Open-Ended Search by a Novelty Guided Evolution Strategy. PPSN (2) 2010: 71-80
2000 – 2009
- 2009
- [c14]Lars Gräning, Markus Olhofer, Bernhard Sendhoff
Interaction Detection in Aerodynamic Design Data. IDEAL 2009: 160-167 - 2008
- [j4]Lars Gräning, Stefan Menzel, Martina Hasenjäger
Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries. J. Math. Model. Algorithms 7(4): 329-350 (2008) - 2007
- [c13]Lars Gräning, Markus Olhofer, Bernhard Sendhoff:
Knowledge Extraction from Unstructured Surface Meshes. IDEAL 2007: 497-506 - 2006
- [c12]Stefan Menzel, Markus Olhofer, Bernhard Sendhoff:
Direct Manipulation of Free Form Deformation in Evolutionary Design Optimisation. PPSN 2006: 352-361 - 2005
- [j3]Frank Hoffmann, Xiao Zhi Gao, Markus Olhofer, Antony Satyadas:
Application reviews. Appl. Soft Comput. 5(3): 261-264 (2005) - [c11]Michael Nashvili, Markus Olhofer, Bernhard Sendhoff
Morphing methods in evolutionary design optimization. GECCO 2005: 897-904 - [c10]Yaochu Jin, Markus Olhofer, Bernhard Sendhoff:
On Evolutionary Optimization of Large Problems Using Small Populations. ICNC (2) 2005: 1145-1154 - 2004
- [j2]Hans-Georg Beyer
On the Impact of Systematic Noise on the Evolutionary Optimization Performance-A Sphere Model Analysis. Genet. Program. Evolvable Mach. 5(4): 327-360 (2004) - [c9]Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff, Markus Olhofer:
Voronoi-based estimation of distribution algorithm for multi-objective optimization. IEEE Congress on Evolutionary Computation 2004: 1594-1601 - [c8]Razvan Enache, Bernhard Sendhoff, Markus Olhofer, Martina Hasenjäger
Comparison of Steady-State and Generational Evolution Strategies for Parallel Architectures. PPSN 2004: 253-262 - [c7]Tatsuya Okabe, Yaochu Jin, Markus Olhofer, Bernhard Sendhoff:
On Test Functions for Evolutionary Multi-objective Optimization. PPSN 2004: 792-802 - 2003
- [c6]Tatsuya Okabe, Kwasi Foli, Markus Olhofer, Yaochu Jin
Comparative studies on micro heat exchanger optimisation. IEEE Congress on Evolutionary Computation 2003: 647-654 - 2002
- [j1]Yaochu Jin
A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 6(5): 481-494 (2002) - [c5]Hans-Georg Beyer, Markus Olhofer, Bernhard Sendhoff:
On the Behavior of (μ/μ1, λ)-ES Optimizing Functions Disturbed by Generalized Noise. FOGA 2002: 307-328 - [c4]Bernhard Sendhoff, Hans-Georg Beyer
The Influence of stochastic Quality Functions on Evolutionary Search. SEAL 2002: 152-172 - 2001
- [c3]Markus Olhofer, Yaochu Jin, Bernhard Sendhoff:
Adaptive encoding for aerodynamic shape optimization using evolution strategies. CEC 2001: 576-583 - [c2]Yaochu Jin, Markus Olhofer, Bernhard Sendhoff:
Managing approximate models in evolutionary aerodynamic design optimization. CEC 2001: 592-599 - 2000
- [c1]Yaochu Jin, Markus Olhofer, Bernhard Sendhoff:
On Evolutionary Optimization with Approximate Fitness Functions. GECCO 2000: 786-793
Coauthor Index
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