Description

Context

macop is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.

Installation

Just install package using pip Python package manager:

pip install macop

How to use ?

Load all macop implemented features:

from macop.algorithms.IteratedLocalSearch import IteratedLocalSearch as ILS
from macop.solutions.BinarySolution import BinarySolution
from macop.evaluators.EvaluatorExample import evaluatorExample

from macop.operators.mutators.SimpleMutation import SimpleMutation
from macop.operators.mutators.SimpleBinaryMutation import SimpleBinaryMutation
from macop.operators.crossovers.SimpleCrossover import SimpleCrossover
from macop.operators.crossovers.RandomSplitCrossover import RandomSplitCrossover

from macop.operators.policies.RandomPolicy import RandomPolicy

from macop.checkpoints.BasicCheckpoint import BasicCheckpoint

# logging configuration
logging.basicConfig(format='%(asctime)s %(message)s', filename='example.log', level=logging.DEBUG)

# default validator
def validator(solution):
   return True

# define init random solution
def init():
   return BinarySolution([], 30).random(validator)

filepath = "checkpoints.csv"

def main():

   operators = [SimpleBinaryMutation(), SimpleMutation(), SimpleCrossover(), RandomSplitCrossover()]
   policy = RandomPolicy(operators)

   algo = ILS(init, evaluatorExample, operators, policy, validator, True)
   algo.addCheckpoint(_class=BasicCheckpoint, _every=5, _filepath=filepath)

   bestSol = algo.run(425)

   print("Found ", bestSol)


if __name__ == "__main__":
   main()