Exploratory landscape analysis
By Olaf Mersmann, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, G. Rudolph. Annual Conference on Genetic and Evolutionary Computation, 2011
412 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Computational Theory and Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e25224aadf3f0a887457715128f13505a0c7c4a2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Bernd Bischl
- Heike Trautmann
- Pascal Kerschke
- F. Neumann
- Claus Weihs
- Holger H. Hoos
- Olaf Mersmann
- Frank Hutter
In Statistics and Probability
- Heike Trautmann
- F. Neumann
- Claus Weihs
- Bernd Bischl
- Holger H. Hoos
- Jakob Bossek
- Pascal Kerschke
- Olaf Mersmann
Citations in the corpus
- Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation (2012, methodology): “This approach of exploratory landscape analysis as contemplated in Mersmann et al. (2010, 2011) offers a possibility for selecting the most appropriate optimization algorithm with respect to the characteristics of the optimization problem considered.”
- Automated Algorithm Selection: Survey and Perspectives (2018, methodology): “With the introduction of Exploratory Landscape Analysis (ELA) , Mersmann et al. (2011) were the first to explicitly develop landscape features for use in black-box optimisation (BBO) – and hence for algorithm selection in continuous optimisation.”
- Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness (2012, background): “For example, in the context of benchmarking algorithms on continuous black-box optimization problems the extraction of problem properties that might influence algorithm performance is an important and current focus of research, denoted as exploratory landscape analysis (ELA, [15]).”