multiObjective_GA
¶
Module Contents¶
Classes¶
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class
MyProblem
(theOptimizationVariables, nbr_objectives, nbr_constraints, evaluationFunction)[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.core.Problem
Automatically sets the optimization problem
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evaluate
(solution)[source]¶ 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.
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class
MyGenerator
(initialVectorGuess)[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.Generator
Population generator to insert initial individual
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class
MaxTimeTerminationCondition
(maxTime)[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.core.TerminationCondition
Abstract class for defining termination conditions.
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class
ManualStopFromFileTermination
(filename)[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.core.TerminationCondition
Abstract class for defining termination conditions.
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class
ConvergenceTerminationCondition
(minrelchange_percent=0.1, nb_generation=15)[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.core.TerminationCondition
Abstract class for defining termination conditions.
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class
SeveralTerminationCondition
[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.core.TerminationCondition
Abstract class for defining termination conditions.
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class
MyMapEvaluator
(callback_on_evaluation)[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.evaluator.Evaluator
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class
MyMultiprocessEvaluator
(callback_on_evaluation, numberOfCores)[source]¶ Bases:
optimeed.optimize.optiAlgorithms.platypus.evaluator.Evaluator
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class
MultiObjective_GA
(theGenerator=MyGenerator)[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 aGenerator
Define the algorithm. As options, define how to evaluate the elements with aEvaluator
, i.e., for multiprocessing. Define what is the termination condition of the algorithm withTerminationCondition
. Here, termination condition is a maximum time.-
initialize
(initialVectorGuess, listOfOptimizationVariables)[source]¶ This function is called just before running optimization algorithm.
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set_evaluationFunction
(evaluationFunction, callback_on_evaluation, numberOfObjectives, numberOfConstraints, array_evaluator)[source]¶ 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)
- evaluationFunction – check
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