# Optimizer using Evolutionary Algorithm¶

## GeneticAlgorithmOptimizer¶

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

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]
end()[source]

## 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