Covariance Matrix Adaptation Evolution Strategy.
| Date | Contributor | Description | Rating |
|---|---|---|---|
| 4 Nov 2010 | Magnus |
The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is an evolutionary algorithm for difficult non-linear non-convex optimization problems in continuous domain. The CMA-ES is typically applied to unconstrained or bounded constraint optimization problems, and search space dimensions between three and a hundred. The method should be applied if derivative based methods, e.g. quasi-Newton BFGS or conjugate gradient, (supposedly) fail due to a rugged search landscape (e.g. discontinuities, sharp bends or ridges, noise, local optima, outliers). If second order derivative based methods are successful, they are usually faster than the CMA-ES: on purely convex-quadratic functions, f(x)=xTHx, BFGS (Matlab function fminunc) is typically faster by a factor of about ten (in terms of number of objective function evaluations needed to reach a target function value, assuming that gradients are not available). On the most simple quadratic function f(x)=||x||2=xTx BFGS is faster by a factor of about 30. |
| Tag | Applied By | Date/Time |
|---|---|---|
| evolutionary algorithms | yasmeen george | 16 May 2011 at 5:18am |
| direct search | Magnus | 5 Nov 2010 at 5:15am |
| evolutionary algorithms | Magnus | 5 Nov 2010 at 5:15am |
| global optimization | Magnus | 5 Nov 2010 at 5:15am |
| optimization | Magnus | 5 Nov 2010 at 5:15am |