modea.Parameters module

This Parameters module is a container for all possible parameters and all ways in which they are adapted by various optimization methods.

class modea.Parameters.BaseParameters

Bases: object

Data holder class for all hardcoded values that are independent of problem dimensionality

alpha_mu = 2
c = 0.817
c_p = 0.08333333333333333
conditioncov = 100000000000000.0
p_target = 0.18181818181818182
p_thresh = 0.44
tolfun = 1e-12
tolupsigma = 1e+20
class modea.Parameters.Parameters(n, budget, sigma=None, mu=None, lambda_=None, weights_option=None, l_bound=None, u_bound=None, seq_cutoff=1, wcm=None, active=False, elitist=False, local_restart=None, sequential=False, tpa=False, values=None)

Bases: modea.Parameters.BaseParameters

Data holder class that initializes all possible parameters, regardless of what functions/algorithm are used If multiple functions/algorithms use the same parameter name, but differently, these will be split into separate parameters.

Parameters:
  • n – Dimensionality of the problem to be solved
  • budget – Number of fitness evaluations the algorithm may perform
  • mu – Number of individuals that form the parents of each generation
  • lambda – Number of individuals in the offspring of each generation
  • weights_option – String to determine which weignts to use. Choose between default (CMA-ES) and 1/n
  • l_bound – Lower bound of the search space
  • u_bound – Upper bound of the search space
  • seq_cutoff – Minimal cut-off allowed in sequential selection
  • wcm – Initial weighted center of mass
  • active – Boolean switch on using an active update. Default: False
  • elitist – Boolean switch on using a (mu, l) strategy rather than (mu + l). Default: False
  • sequential – Boolean switch on using sequential evaluation. Default: False
  • tpa – Boolean switch on using two-point step-size adaptation. Default: False
  • values – Dictionary in the form of {'name': value} of initial values for allowed parameters. Any values for names not in modea.Utils.initializable_parameters are ignored.
adaptCovarianceMatrix(evalcount)

Adapt the covariance matrix according to the (Active-)CMA-ES.

Parameters:evalcount – Number of evaluations used by the algorithm so far
addToFitnessHistory(fitness)

Record the latest fitness value (with a history of 5 generations)

Parameters:fitness – Fitness value to be recorded
addToSuccessHistory(t, success)

Record the (boolean) success value at time t

Parameters:
  • t – Number of evaluations used by the algorithm so far
  • success – Boolean that records whether the last update was a success
checkDegenerated()

Check if the parameters (C, s_mean, etc) have degenerated and need to be reset. Designed for use by a CMA ES

checkLocalRestartConditions(evalcount)

Check for local restart conditions according to (B)IPOP

Parameters:evalcount – Counter for the current generation
Returns:Boolean value restart_required, True if a restart should be performed
getParameterOpts()
getWeights(weights_option=None)
Defines a list of weights to be used in weighted recombination. Available options are:

  • 1/n: Each weight is set to 1/n

  • 1/2^n: Each weight is set to 1/2^i + (1/2^n)/mu

  • default: Each weight is set to log((lambda-1)/2) - log(i)

    param weights_option:
     String to indicate which weights should be used.
    returns:Returns a np.array of weights, adding to 1
mu_int

Integer value of mu

oneFifthRule(t)

Adapts sigma based on the 1/5-th success rule

Parameters:t – Number of evaluations used by the algorithm so far
recordRecentFitnessValues(evalcount, fitnesses)

Record recent fitness values at current budget

restart()

Very basic restart, done by resetting some of the variables for CMA-ES

updateThreshold(t)

Update the threshold that is used to maintain a minimum stepsize. Taken from: Evolution Strategies with Thresheld Convergence (CEC 2015)

Parameters:t – Ammount of the evaluation budget spent