bluepyopt.deapext.optimisations

Optimisation class

class bluepyopt.deapext.optimisations.DEAPOptimisation(evaluator=None, use_scoop=False, seed=1, offspring_size=10, eta=10, mutpb=1.0, cxpb=1.0, map_function=None, hof=None, selector_name=None)[source]

Bases: bluepyopt.optimisations.Optimisation

DEAP Optimisation class

Constructor

Parameters:
  • evaluator (Evaluator) – Evaluator object
  • seed (float) – Random number generator seed
  • offspring_size (int) – Number of offspring individuals in each generation
  • eta (float) – Parameter that controls how far the crossover and
  • operator disturbe the original individuals (mutation) –
  • mutpb (float) – Mutation probability
  • cxpb (float) – Crossover probability
  • map_function (function) – Function used to map (parallelise) the evaluation function calls
  • hof (hof) – Hall of Fame object
  • selector_name (str) – The selector used in the evolutionary algorithm, possible values are ‘IBEA’ or ‘NSGA2’
run(max_ngen=10, offspring_size=None, continue_cp=False, cp_filename=None, cp_frequency=1)[source]

Run optimisation

setup_deap()[source]

Set up optimisation

class bluepyopt.deapext.optimisations.IBEADEAPOptimisation(*args, **kwargs)[source]

Bases: bluepyopt.deapext.optimisations.DEAPOptimisation

IBEA DEAP class

Constructor

class bluepyopt.deapext.optimisations.WSListIndividual(*args, **kwargs)[source]

Bases: list

Individual consisting of list with weighted sum field

Constructor

class bluepyopt.deapext.optimisations.WeightedSumFitness(values=(), obj_size=None)[source]

Bases: deap.base.Fitness

Fitness that compares by weighted sum

sum

Weighted sum of values

weighted_sum

Weighted sum of wvalues