5. Use of evaluators

Now that it is possible to generate a solution randomly or not. It is important to know the value associated with this solution. We will then speak of evaluation of the solution. With the score associated with it, the fitness.

5.1. Generic evaluator

As for the management of solutions, a generic evaluator class macop.evaluators.base.Evaluator is developed within Macop:

Abstract Evaluator class is used for computing fitness score associated to a solution. To evaluate all the solutions, this class:

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

• has a compute abstract method enable to compute and associate a score to a given solution

• stores into its algo attritute the current algorithm to use (we will talk about algorithm later)

We must therefore now create our own evaluator based on the proposed structure.

5.2. Custom evaluator

To create our own evaluator, we need both:

• data useful for evaluating a solution

• calculate the score (fitness) associated with the state of the solution from these data. Hence, implement specific compute method.

We will define the KnapsackEvaluator class, which will therefore allow us to evaluate solutions to our current problem.

"""
modules imports
"""
from macop.evaluators.base import Evaluator

class KnapsackEvaluator(Evaluator):

    def compute(solution):

        # `_data` contains worths array values of objects
        fitness = 0
        for index, elem in enumerate(solution.getdata = )):
            fitness += self._data['worths'][index] * elem

        return fitness

It is now possible to initialise our new evaluator with specific data of our problem instance:

"""
Problem instance definition
"""
elements_score = [ 4, 2, 10, 1, 2 ] # worth of each object
elements_weight = [ 12, 1, 4, 1, 2 ] # weight of each object

"""
Evaluator problem instance
"""
evaluator = KnapsackEvaluator(data={'worths': elements_score})

# using defined BinarySolution
solution = BinarySolution.random(5)

# obtaining current solution score
solution_fitness = solution.evaluate(evaluator)

# score is also stored into solution
solution_fitness = solution.fitness

Note

The current developed KnapsackEvaluator is available into macop.evaluators.mono.KnapsackEvaluator in Macop.

In the next part we will see how to modify our current solution with the use of modification operator.