Michael Emmerich
Papers
1
Total Citations
11
H-Index
1
About
Dr. Michael Emmerich is a leading figure in computational intelligence, with a core focus on evolutionary multi-objective optimization, design-space exploration, and surrogate-assisted modeling. His pioneering work bridges the gap between black-box optimization and human-centric design, most notably through his influential 2013 paper on "Novelty and interestingness measures for design-space exploration." This highly cited contribution (11 citations) provides a unifying framework that translates psychological concepts of novelty and interestingness into formal metrics, enabling more creative and efficient exploration of complex design spaces. Beyond this, Emmerich has made foundational contributions to indicator-based evolutionary algorithms and the use of Gaussian process models for expensive optimization problems. His research has had a profound impact on fields ranging from engineering design to bioinformatics, where his methods help practitioners navigate high-dimensional, multi-objective landscapes. A respected educator and collaborator, Emmerich continues to shape the next generation of optimization techniques, ensuring that computational search remains both powerful and intuitively aligned with human design goals.
Research Focus
Key Achievements
Top Papers
- 1Novelty and interestingness measures for design-space exploration11 citations · 2013