CMA-ES with Two-Point Step-Size Adaptation
| dc.creator | Hansen, Nikolaus | |
| dc.date | 2008-05-02 | |
| dc.date | 2008-05-18 | |
| dc.date.accessioned | 2026-07-07T12:18:33Z | |
| dc.date.available | 2026-07-07T12:18:33Z | |
| dc.description | We combine a refined version of two-point step-size adaptation with the covariance matrix adaptation evolution strategy (CMA-ES). Additionally, we suggest polished formulae for the learning rate of the covariance matrix and the recombination weights. In contrast to cumulative step-size adaptation or to the 1/5-th success rule, the refined two-point adaptation (TPA) does not rely on any internal model of optimality. In contrast to conventional self-adaptation, the TPA will achieve a better target step-size in particular with large populations. The disadvantage of TPA is that it relies on two additional objective function | |
| dc.identifier | https://arxiv.org/abs/0805.0231 | |
| dc.identifier | http://arxiv.org/abs/0805.0231 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/212450 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.title | CMA-ES with Two-Point Step-Size Adaptation | |
| dc.type | text |