multiObjective_GA

Module Contents

Classes

class MyProblem(theOptimizationVariables, nbr_objectives, nbr_constraints, evaluationFunction)

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

Automatically sets the optimization problem

evaluate(solution)

Evaluates the problem.

By default, this method calls the function passed to the constructor. Alternatively, a problem can subclass and override this method. When overriding, this method is responsible for updating the objectives and constraints stored in the solution.

solution: Solution

The solution to evaluate.

class MyGenerator(initialVectorGuess)

Bases: optimeed.optimize.optiAlgorithms.platypus.Generator

Population generator to insert initial individual

generate(problem)

class MaxTimeTerminationCondition(maxTime)

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

Abstract class for defining termination conditions.

initialize(algorithm)

Initializes this termination condition.

This method is used to collect any initial state, such as the current NFE or current time, needed for calculating the termination criteria.

algorithm : Algorithm

The algorithm being run.

shouldTerminate(algorithm)

Checks if the algorithm should terminate.

Check the termination condition, returning True if the termination condition is satisfied; False otherwise. This method is called after each iteration of the algorithm.

algorithm : Algorithm

The algorithm being run.

class ManualStopFromFileTermination(filename)

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

Abstract class for defining termination conditions.

shouldTerminate(algorithm)

Checks if the algorithm should terminate.

Check the termination condition, returning True if the termination condition is satisfied; False otherwise. This method is called after each iteration of the algorithm.

algorithm : Algorithm

The algorithm being run.

class ConvergenceTerminationCondition(minrelchange_percent=0.1, nb_generation=15)

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

Abstract class for defining termination conditions.

initialize(algorithm)

Initializes this termination condition.

This method is used to collect any initial state, such as the current NFE or current time, needed for calculating the termination criteria.

algorithm : Algorithm

The algorithm being run.

shouldTerminate(algorithm)

Checks if the algorithm should terminate.

Check the termination condition, returning True if the termination condition is satisfied; False otherwise. This method is called after each iteration of the algorithm.

algorithm : Algorithm

The algorithm being run.

class SeveralTerminationCondition

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

Abstract class for defining termination conditions.

initialize(algorithm)

Initializes this termination condition.

This method is used to collect any initial state, such as the current NFE or current time, needed for calculating the termination criteria.

algorithm : Algorithm

The algorithm being run.

add(theTerminationCondition)

shouldTerminate(algorithm)

Checks if the algorithm should terminate.

Check the termination condition, returning True if the termination condition is satisfied; False otherwise. This method is called after each iteration of the algorithm.

algorithm : Algorithm

The algorithm being run.

class MyMapEvaluator(callback_on_evaluation)

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

evaluate_all(jobs, **kwargs)

class MyMultiprocessEvaluator(callback_on_evaluation, numberOfCores)

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

my_callback(output)

evaluate_all(jobs, **kwargs)

close()

class MultiObjective_GA(theGenerator=MyGenerator)

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.

initialize(initialVectorGuess, listOfOptimizationVariables)

This function is called just before running optimization algorithm.

compute()

Launch the optimization

Returns:vector of optimal variables

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

Set the evaluation function and all the necessary callbacks

Parameters:

• evaluationFunction – check evaluateObjectiveAndConstraints()
• callback_on_evaluation – check callback_on_evaluation(). Call this function after performing the evaluation of the individuals
• numberOfObjectives – int, number of objectives
• numberOfConstraints – int, number of constraints
• array_evaluator – If True, evaluate each generation at once using numpy array. Use it only with care, as it dismisses some features (expert mode)

set_maxtime(maxTime)

Set maximum optimization time (in seconds)

__str__()

Return str(self).

get_convergence()

This function is called just before compute. Because the convergence is contained in opti algorithm, it must be created now.

add_terminationCondition(theTerminationCondition)

reset()