Michael T. M. Emmerich

Computational Theory and Mathematics · 2005–2024

A researcher in the Science corpus: 7 papers; trusts 49 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 76 citations into it; #434 of 12,182 by trust here, #836 by citations
• Artificial Intelligence — 71 citations into it; #4,411 of 35,345 by trust here, #9,170 by citations
• Management Science and Operations Research — 25 citations into it; #930 of 11,946 by trust here, #1,559 by citations
• Statistics and Probability — 12 citations into it; #3,881 of 12,932 by trust here, #4,371 by citations
• Numerical Analysis — 11 citations into it
• Control and Systems Engineering — 8 citations into it; #5,916 of 8,257 by trust here, #6,764 by citations
• Evaluation & Benchmarks — 7 citations into it
• Mathematical Optimization — 7 citations into it

Whom Michael T. M. Emmerich trusts overall

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

1. Kalyanmoy Deb
2. Eckart Zitzler
3. Joshua D. Knowles
4. Carlos Artemio Coello Coello
5. Yaochu Jin
6. Marco Laumanns
7. Hans-Georg Beyer
8. David Wolfe Corne
9. Lothar Thiele
10. Thomas Bäck

Who trusts Michael T. M. Emmerich overall

1. Yaochu Jin
2. Ye Tian
3. Kalyanmoy Deb
4. Handing Wang
5. Matthias Ehrgott
6. Joshua D. Knowles
7. Eckart Zitzler
8. Ran Cheng
9. Xing-yi Zhang
10. Qingfu Zhang

Papers from Michael T. M. Emmerich's perspective

Ordered by how strongly the researchers Michael T. M. Emmerich trusts endorse each paper. Same corpus, a different order for every researcher.

1. Multi-objective optimization using evolutionary algorithms
2. Simulated Binary Crossover for Continuous Search Space
3. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
4. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy (Evolutionary Computation)
5. Indicator-Based Selection in Multiobjective Search (Parallel Problem Solving from Nature)
6. Genetic Algorithms in Search
7. Performance assessment of multiobjective optimizers: an analysis and review (IEEE Transactions on Evolutionary Computation)
8. ImageNet classification with deep convolutional neural networks
9. Evolutionary Algorithms for Solving Multi-Objective Problems
10. A fast and elitist multiobjective genetic algorithm: NSGA-II

Their papers

• SMS-EMOA: Multiobjective selection based on dominated hypervolume (2007) — 2,020 citations
• An EMO Algorithm Using the Hypervolume Measure as Selection Criterion (2005) — 670 citations
• Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels (2006) — 783 citations
• Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies (2024) — 648 citations
• A new acquisition function for Bayesian optimization based on the moment-generating function (2017) — 78 citations
• Cooling Strategies for the Moment-Generating Function in Bayesian Global Optimization (2018) — 21 citations
• A tutorial on multiobjective optimization: fundamentals and evolutionary methods (2018) — 679 citations