Covariance Matrix Adaptation Evolution Strategy.
|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.
|optimization||sarath||19 Nov 2013 at 1:08pm|
|global optimization||sarath||19 Nov 2013 at 1:08pm|
|evolutionary algorithms||sarath||19 Nov 2013 at 1:08pm|
|direct search||sarath||19 Nov 2013 at 1:08pm|
|evolutionary algorithms||yasmeen george||16 May 2011 at 5:18am|
|evolutionary algorithms||Magnus||5 Nov 2010 at 5:15am|
|direct search||Magnus||5 Nov 2010 at 5:15am|
|global optimization||Magnus||5 Nov 2010 at 5:15am|
|optimization||Magnus||5 Nov 2010 at 5:15am|