macop.evaluators.discrete.multi

Multi-objective evaluators classes

Classes

WeightedSum(data)

Weighted-sum sub-evaluator class which enables to compute solution using specific _data

class macop.evaluators.discrete.multi.WeightedSum(data)

Weighted-sum sub-evaluator class which enables to compute solution using specific _data

• stores into its _data dictionary attritute required measures when computing a solution

• _data[‘evaluators’] current evaluator to use

• _data[‘weights’] Associated weight to use

• compute method enables to compute and associate a tuples of scores to a given solution

>>> import random
>>> # binary solution import
>>> from macop.solutions.discrete import BinarySolution
>>> # evaluators imports
>>> from macop.evaluators.discrete.mono import KnapsackEvaluator
>>> from macop.evaluators.discrete.multi import WeightedSum
>>> solution_data = [1, 0, 0, 1, 1, 0, 1, 0]
>>> size = len(solution_data)
>>> solution = BinarySolution(solution_data, size)
>>> # evaluator 1 initialization (worths objects passed into data)
>>> worths1 = [ random.randint(5, 20) for i in range(size) ]
>>> evaluator1 = KnapsackEvaluator(data={'worths': worths1})
>>> # evaluator 2 initialization (worths objects passed into data)
>>> worths2 = [ random.randint(10, 15) for i in range(size) ]
>>> evaluator2 = KnapsackEvaluator(data={'worths': worths2})
>>> weighted_evaluator = WeightedSum(data={'evaluators': [evaluator1, evaluator2], 'weights': [0.3, 0.7]})
>>> weighted_score = weighted_evaluator.compute(solution)
>>> expected_score = evaluator1.compute(solution) * 0.3 + evaluator2.compute(solution) * 0.7
>>> weighted_score == expected_score
True
>>> weighted_score
50.8

compute(solution)

Apply the computation of fitness from solution

• Associate tuple of fitness scores for each objective to the current solution

• Compute weighted-sum for these objectives

Parameters

solution – {Solution} – Solution instance

Returns

{float} – weighted-sum of the fitness scores