This optimization module is an implementation of the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). It uses the source code of Nikolaus Hansen ( https://www.lri.fr/~hansen/cmaes_inmatlab.html#java ).
Among the tree evolutionary operators (Recombination, Mutation, Selection), the mutation is considered the most important. Mutation is performed by sampling a Covariance Matrix. The parameters of the matrix (variances and correlation coefficients) are adapted by tracking the path of successful mutations.
|Algorithm||stochastic - generates new solutions by sampling a probability function, however deterministic adaptation of the covariance matrix.|
|Design Variables||Written for continuous variables. No discrete or mixed variables are possible.|
|Objectives||single-objective for minimization.|
|Constraint handling|| no, use e.g. the Module [[Documentation/Modules/ConstraintHandler | |
|Boundary handling|| yes, the module [[Documentation/Modules/BoundHandler | |
|Starting at this module|| Module requires exactly one connection of type |
|Ending at this module||-|
|Run||starts the optimization.|
The options are currently only described in the references (see below).
The algorithm distinguishes several cases, depending on the problem properties (
initial solution, initial search region, bounds).
The algorithm contains stochastic processes and operates with a population. Parallelization on the basis of the population size is implemented.
Detailed information is given at http://www.lri.fr/~hansen/ .