Optimizer using Evolutionary Algorithm

GeneticAlgorithmOptimizer

class l2l.optimizers.evolution.optimizer.GeneticAlgorithmOptimizer(traj, optimizee_create_individual, optimizee_fitness_weights, parameters, optimizee_bounding_func=None)[source]

Bases: l2l.optimizers.optimizer.Optimizer

Implements evolutionary algorithm

Parameters:
  • traj (Trajectory) – Use this trajectory to store the parameters of the specific runs. The parameters should be initialized based on the values in parameters
  • optimizee_create_individual – Function that creates a new individual
  • optimizee_fitness_weights – Fitness weights. The fitness returned by the Optimizee is multiplied by these values (one for each element of the fitness vector)
  • parameters – Instance of namedtuple() GeneticAlgorithmOptimizer containing the parameters needed by the Optimizer
post_process(traj, fitnesses_results)[source]

See post_process()

end()[source]

See end()

GeneticAlgorithmParameters

class l2l.optimizers.evolution.optimizer.GeneticAlgorithmParameters

Bases: tuple

Parameters:
  • seed – Random seed
  • popsize – Size of the population
  • CXPB – Crossover probability
  • MUTPB – Mutation probability
  • NGEN – Number of generations simulation should run for
  • indpb – Probability of mutation of each element in individual
  • tournsize – Size of the tournamaent used for fitness evaluation and selection
  • matepar – Paramter used for blending two values during mating
CXPB
MUTPB
NGEN
indpb
matepar
mutpar
popsize
seed
tournsize