Bernhard Sendhoff

Artificial Intelligence · 2000–2017

A researcher in the Science corpus: 12 papers; trusts 63 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #3,282 of 35,345 by trust here, #5,720 by citations
• Computational Theory and Mathematics — 117 citations into it; #411 of 12,182 by trust here, #527 by citations
• Management Science and Operations Research — 26 citations into it; #2,775 of 11,946 by trust here, #4,118 by citations
• Statistics and Probability — 13 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Control and Systems Engineering — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Bernhard Sendhoff trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kalyanmoy Deb
2. Yaochu Jin
3. Eckart Zitzler
4. Qingfu Zhang
5. Xin Yao
6. Eric R. Ziegel
7. Christian Igel
8. Sebastian Thrun
9. Hui Li
10. Lothar Thiele

Who trusts Bernhard Sendhoff overall

1. Yaochu Jin
2. Ye Tian
3. Xing-yi Zhang
4. Ran Cheng
5. Handing Wang
6. Tinkle Chugh
7. Guo Yu
8. Yashesh D. Dhebar
9. Torsten Hildebrandt
10. Xiutao Zheng

Papers from Bernhard Sendhoff's perspective

Ordered by how strongly the researchers Bernhard Sendhoff trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Multi-objective optimization using evolutionary algorithms
3. A fast and elitist multiobjective genetic algorithm: NSGA-II
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Simulated Binary Crossover for Continuous Search Space
6. Gradient-based learning applied to document recognition
7. Indicator-Based Selection in Multiobjective Search (Parallel Problem Solving from Nature)
8. Performance assessment of multiobjective optimizers: an analysis and review (IEEE Transactions on Evolutionary Computation)
9. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Optimization for problem classes-neural networks that learn to learn (2000) — 33 citations
• A framework for evolutionary optimization with approximate fitness functions (2002) — 692 citations
• A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016) — 1,488 citations
• Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion (2006) — 343 citations
• A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015) — 330 citations
• Generalizing Surrogate-Assisted Evolutionary Computation (2010) — 445 citations
• Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017) — 351 citations
• Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies (2008) — 467 citations
• The Influence of Learning on Evolution: A Mathematical Framework (2009) — 52 citations
• Optimization of micro heat exchanger: CFD, analytical approach and multi-objective evolutionary algorithms (2006) — 214 citations
• Creating Brain-Like Intelligence (2009) — 33 citations
• Influence of Plasticity and Learning on Evolution under Directional Selection (2007) — 127 citations