CMA#

This refers to the covariance matrix adaptation evolutionary optimzation strategy Background: https://en.wikipedia.org/wiki/CMA-ES

The following parameters are changed

diagonal

To use the diagonal version of CMA (advised in large dimensions)

  • True : Use diagonal

  • False : Don’t use diagonal

fcmaes

To use fast implementation, doesn’t support diagonal=True. produces equivalent results, preferable for high dimensions or if objective function evaluation is fast.

‘CMA’#

diagonal: False
fcmaes: False

‘DiagonalCMA’#

diagonal: True
fcmaes: False

‘FCMA’#

diagonal: False
fcmaes: True

Further variants of CMA include CMA with test based population size adaption. It sets Population-size equal to lambda = 4 x dimension. It further introduces the parameters:

popsize_adaption

To use CMA with popsize adaptation

  • True : Use popsize adaptation

  • False : Don’t…

covariance_memory

Use covariance_memory

  • True : Use covariance

  • False : Don’t…

‘EDA’#

popsize_adaption: False
covariance_memory: False

‘PCEDA’#

popsize_adaption: True
covariance_memory: False

‘MPCEDA’#

popsize_adaption: True
covariance_memory: True

‘MEDA’#

popsize_adaption: False
covariance_memory: True