Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
By Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen Finck, P. Posík. Annual Conference on Genetic and Evolutionary Computation, 2010
515 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/997dbb58a988bd1fa6ab22a599811816277f2a51
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Anne Auger
- Nikolaus Hansen
- Dimo Brockhoff
- Zyed Bouzarkouna
- Didier Yu Ding
- Olaf Mersmann
- Tea Tusar
- Hado van Hasselt
In Computational Theory and Mathematics
- Anne Auger
- Dimo Brockhoff
- Nikolaus Hansen
- Zyed Bouzarkouna
- Didier Yu Ding
- Olaf Mersmann
- Tea Tusar
- Stefano Forli
Citations in the corpus
- Irrigation scheduling of a classical gravity network based on the Covariance Matrix Adaptation - Evolutionary Strategy algorithm (2014, methodology): “Benchmarking of several algorithms has established that CMA-ES is one of the most efficient metaheuristics for dealing with difficult numerical optimization problems (Hansen et al., 2010).”
- Reinforcement Learning in Continuous State and Action Spaces (2012, background): “We choose this particular paper becausit reports results for NAC and for CMA-ES, which is considered the current state-of-the-art in direct policy search and black-box optimization (Jiang et al, 2008; Gomez et al, 2008; Glasmachers et al, 2010; Hansen et al, 2010).”
- COCO: a platform for comparing continuous optimizers in a black-box setting (2016, methodology): “s. Therefore, we call our approach budgetfree. Restarts however “improve the reliability, comparability, precision, and “visibility” of the measured results” [HAN2016ex]. Simulated restarts[HAN2010ex][HAN2010][BBO2016perf] are used to determine a runtime for unsuccessful runs. Semantically, this is ”
- Accelerating AutoDock4 with GPUs and Gradient-Based Local Search (2019, background): “In the field of global optimization, computational effort is often expressed as the number of fitness (or score) evaluations (38).”
- Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models (2011, methodology): “Though the gain brought by coupling meta-models to CMA-ES can seem rather small, we think that it is mainly due to the fact that CMA-ES is almost optimal [29] and thus hard to improve by an important factor.”