multiObjective_GA

Module Contents

class MyConvergence(*args, **kwargs)

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

conv :EvolutionaryConvergence

extend(self, solutions)

get_graphs(self)

class MyProblem(theOptimizationVariables, nbr_objectives, nbr_constraints, evaluationFunction)

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

Automatically sets the optimization problem

evaluate(self, solution)

class MyGenerator(initialVectorGuess)

Bases: optimeed.optimize.optiAlgorithms.platypus.Generator

Population generator to insert initial individual

generate(self, problem)

class MyTerminationCondition(maxTime)

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

initialize(self, algorithm)

shouldTerminate(self, algorithm)

class MyMapEvaluator(callback_on_evaluation)

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

evaluate_all(self, jobs, **kwargs)

class MyMultiprocessEvaluator(callback_on_evaluation, numberOfCores)

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

evaluate_all(self, jobs, **kwargs)

close(self)

class MultiObjective_GA

Bases: optimeed.optimize.optiAlgorithms.algorithmInterface.AlgorithmInterface

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)

set_evaluationFunction(self, evaluationFunction, callback_on_evaluation, numberOfObjectives, numberOfConstraints)

set_maxtime(self, maxTime)

__str__(self)

get_convergence(self)