optiAlgorithms

Package Contents

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]