Alternative Restart Strategies for CMA-ES
By Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. 2012
47 citations recorded by the source corpus.
This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Optimization & Training
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
- Frank Hutter
- Ilya Loshchilov
- Quoc V. Le
- Dan Hendrycks
- Xuanyi Dong
- Christoph Feichtenhofer
- Ronghang Hu
- Yi Yang
In Computational Theory and Mathematics
- Ilya Loshchilov
- Frank Hutter
- Holger H. Hoos
- Kevin Leyton-Brown
- Aaron Klein
- Marco F. Huber
- Marc-André Zöller
- Katharina Eggensperger
In Optimization & Training
- Frank Hutter
- Ilya Loshchilov
- Ronghang Hu
- Dan Hendrycks
- Quoc V. Le
- Laurence Aitchison
- Christoph Feichtenhofer
- Michael Auli
Citations in the corpus
- SGDR: Stochastic Gradient Descent with Warm Restarts (2016, background): “Instead, the current state-of-the-art gradient-free optimizers employ various restart mechanisms (Hansen, 2009; Loshchilov et al., 2012).”
- SGDR: Stochastic Gradient Descent with Restarts (2016, background): “Instead, the current state-of-the-art gradient-free optimizers employ various restart mechanisms (Hansen, 2009; Loshchilov et al., 2012).”