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Bernhard Sendhoff

Bernhard Sendhoff

D-Index & Metrics

Computer Science

D-Index
52
Citations
15537
World Ranking
4985
National Ranking
222

Research.com Recognitions

  • 2012 - ACM Senior Member

Overview

Bernhard Sendhoff is affiliated with Honda in Germany, contributing extensively to the fields of computer science and engineering. Their research spans various subfields including artificial intelligence, industrial and manufacturing engineering, computational theory and mathematics, computational mechanics, as well as computer graphics and computer-aided design.

Sendhoff's work covers a range of topics, with a significant focus on advanced multi-objective optimization algorithms, vehicle routing optimization methods, 3D shape modeling and analysis, optimization and packing problems, metaheuristic optimization algorithms research, computer graphics and visualization techniques, and manufacturing process and optimization.

Frequent co-authors in Sendhoff's research include Stefan Menzel, Xin Yao, Leandro L. Minku, Thomas Bäck, and Gan Ruan, reflecting collaboration in diverse aspects of computational research and optimization.

Publications by Sendhoff appear regularly in notable venues such as Zenodo (CERN European Organization for Nuclear Research), IEEE Transactions on Evolutionary Computation, the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), arXiv (Cornell University), and the Proceedings of the Genetic and Evolutionary Computation Conference Companion. These venues have hosted multiple works associated with Sendhoff, indicating engagement with both theoretical and applied computational communities.

Recent papers authored or co-authored by Sendhoff include:

  • Cooperative Intelligence - A Humane Perspective, 2020, 2020 IEEE International Conference on Human-Machine Systems (ICHMS)
  • Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation, 2021, IEEE Transactions on Evolutionary Computation
  • A hybrid local search framework for the dynamic capacitated arc routing problem, 2021, Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • A Novel Generalized Metaheuristic Framework for Dynamic Capacitated Arc Routing Problems, 2022, IEEE Transactions on Evolutionary Computation
  • Exploiting Generative Models for Performance Predictions of 3D Car Designs, 2021, 2021 IEEE Symposium Series on Computational Intelligence (SSCI)

Bernhard Sendhoff was recognized as an ACM Senior Member in 2012, a distinction marking their standing within the computing research community.

Best Publications

  • Robust Optimization - A Comprehensive Survey

    Hans-Georg Beyer;Bernhard Sendhoff

  • A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

    Ran Cheng;Yaochu Jin;Markus Olhofer;Bernhard Sendhoff

  • A framework for evolutionary optimization with approximate fitness functions

    Yaochu Jin;M. Olhofer;B. Sendhoff

  • Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies

    Yaochu Jin;B. Sendhoff

  • Generalizing Surrogate-Assisted Evolutionary Computation

    Dudy Lim;Yaochu Jin;Yew-Soon Ong;Bernhard Sendhoff

  • A critical survey of performance indices for multi-objective optimisation

    T. Okabe;Y. Jin;B. Sendhoff

  • Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

    Ran Cheng;Yaochu Jin;Markus Olhofer;Bernhard sendhoff

  • A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling

    Ran Cheng;Yaochu Jin;Kaname Narukawa;Bernhard Sendhoff

  • Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion

    Aimin Zhou;Yaochu Jin;Qingfu Zhang;B. Sendhoff

  • Dynamic Weighted Aggregation for evolutionary multi-objective optimization: why does it work and how?

    Yaochu Jin;Markus Olhofer;Bernhard Sendhoff

  • Optimization of micro heat exchanger: CFD, analytical approach and multi-objective evolutionary algorithms

    Kwasi Foli;Tatsuya Okabe;Markus Olhofer;Yaochu Jin

  • Trade-off between performance and robustness: An evolutionary multiobjective approach

    Yaochu Jin;Bernhard Sendhoff

  • On generating FC/sup 3/ fuzzy rule systems from data using evolution strategies

    Yaochu Jin;W. Von Seelen;B. Sendhoff

  • Efficient Hierarchical Parallel Genetic Algorithms using Grid computing

    Dudy Lim;Yew-Soon Ong;Yaochu Jin;Bernhard Sendhoff

  • Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization

    Aimin Zhou;Yaochu Jin;Qingfu Zhang;Bernhard Sendhoff

  • A systems approach to evolutionary multiobjective structural optimization and beyond

    Yaochu Jin;B. Sendhoff

  • On evolutionary optimization with approximate fitness functions

    Yaochu Jin;Markus Olhofer;Bernhard Sendhoff

  • Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles

    Yaochu Jin;Bernhard Sendhoff

  • Adapting Weighted Aggregation for Multiobjective Evolution Strategies

    Yaochu Jin;Tatsuya Okabe;Bernhard Sendhoff

  • Neural network regularization and ensembling using multi-objective evolutionary algorithms

    Yaochu Jin;T. Okabe;B. Sendhoff

  • Constructing Dynamic Optimization Test Problems Using the Multi-objective Optimization Concept

    Yaochu Jin;Bernhard Sendhoff

Frequent Co-Authors

Yaochu Jin
Yaochu Jin Westlake University
Xin Yao
Xin Yao Lingnan University
Hans-Georg Beyer
Hans-Georg Beyer Vorarlberg University of Applied Sciences
Yew-Soon Ong
Yew-Soon Ong Nanyang Technological University
Qingfu Zhang
Qingfu Zhang City University of Hong Kong
Ke Tang
Ke Tang Southern University of Science and Technology
Edward Tsang
Edward Tsang University of Essex
Thomas Bäck
Thomas Bäck Leiden University
Bu-Sung Lee
Bu-Sung Lee Nanyang Technological University
Hisao Ishibuchi
Hisao Ishibuchi Southern University of Science and Technology

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