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Contents
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This optimization module is an implementation of the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES).
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. |
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| Design Variables | Written for continuous variables. No discrete or mixed variables are possible. |
| Objectives | single-objective for minimization. |
| Constraint handling | no |
| Boundary handling | yes |
| Initialization | - |
| Starting at this module | Module requires exactly one connection of type optimization.
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| Ending at this module | - |
| Name | Description |
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| 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.
... todo
Detailed information is given at http://www.lri.fr/~hansen/ .
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