multiObjective_GA

Module Contents

class My_OMOPSO(problem, epsilons, **kwargs)[source]

Bases: optimeed.optimize.optiAlgorithms.platypus.algorithms.OMOPSO

class MyConvergence(*args, **kwargs)[source]

Bases: optimeed.optimize.optiAlgorithms.convergence.InterfaceConvergence, optimeed.optimize.optiAlgorithms.platypus.core.Archive

conv :EvolutionaryConvergence
extend(self, solutions)[source]
get_graphs(self)[source]
class MyProblem(theOptimizationVariables, nbr_objectives, nbr_constraints, evaluationFunction)[source]

Bases: optimeed.optimize.optiAlgorithms.platypus.core.Problem

Automatically sets the optimization problem

evaluate(self, solution)[source]
class MyGenerator(initialVectorGuess)[source]

Bases: optimeed.optimize.optiAlgorithms.platypus.Generator

Population generator to insert initial individual

generate(self, problem)[source]
class MyTerminationCondition(maxTime)[source]

Bases: optimeed.optimize.optiAlgorithms.platypus.core.TerminationCondition

initialize(self, algorithm)[source]
shouldTerminate(self, algorithm)[source]
class MyMapEvaluator(callback_on_evaluation)[source]

Bases: optimeed.optimize.optiAlgorithms.platypus.evaluator.Evaluator

evaluate_all(self, jobs, **kwargs)[source]
class MyMultiprocessEvaluator(callback_on_evaluation, numberOfCores)[source]

Bases: optimeed.optimize.optiAlgorithms.platypus.evaluator.Evaluator

evaluate_all(self, jobs, **kwargs)[source]
close(self)[source]
class MultiObjective_GA[source]

Bases: optimeed.optimize.optiAlgorithms.algorithmInterface.AlgorithmInterface, optimeed.core.Option_class

Based on Platypus Library. Workflow: Define what to optimize and which function to call with a Problem Define the initial population with a Generator Define the algorithm. As options, define how to evaluate the elements with a Evaluator, i.e., for multiprocessing. Define what is the termination condition of the algorithm with TerminationCondition. Here, termination condition is a maximum time.

DIVISION_OUTER = 0
OPTI_ALGORITHM = 1
NUMBER_OF_CORES = 2
compute(self, initialVectorGuess, listOfOptimizationVariables)[source]
set_evaluationFunction(self, evaluationFunction, callback_on_evaluation, numberOfObjectives, numberOfConstraints)[source]
set_maxtime(self, maxTime)[source]
__str__(self)[source]
get_convergence(self)[source]