"""Policy class implementation which is used for selecting operator using Upper Confidence Bound
"""
# main imports
import logging
import random
import math
# module imports
from .Policy import Policy
[docs]class UCBPolicy(Policy):
"""UCB policy class which is used for applying UCB strategy when selecting and applying operator
Attributes:
operators: {[Operator]} -- list of selected operators for the algorithm
C: {float} -- tradeoff between EvE parameter for UCB
rewards: {[float]} -- list of summed rewards obtained for each operator
occurences: {[int]} -- number of use (selected) of each operator
"""
def __init__(self, _operators, _C=100.):
self.operators = _operators
self.rewards = [0. for o in self.operators]
self.occurences = [0 for o in self.operators]
self.C = _C
[docs] def select(self):
"""Select randomly the next operator to use
Returns:
{Operator}: the selected operator
"""
indices = [i for i, o in enumerate(self.occurences) if o == 0]
# if operator have at least be used one time
if len(indices) == 0:
ucbValues = []
nVisits = sum(self.occurences)
for i in range(len(self.operators)):
ucbValue = self.rewards[i] + self.C * math.sqrt(
math.log(nVisits) / self.occurences[i])
ucbValues.append(ucbValue)
return self.operators[ucbValues.index(max(ucbValues))]
else:
return self.operators[random.choice(indices)]
[docs] def apply(self, _solution):
"""
Apply specific operator chosen to create new solution, computes its fitness and returns solution
Args:
_solution: {Solution} -- the solution to use for generating new solution
Returns:
{Solution} -- new generated solution
"""
operator = self.select()
logging.info("---- Applying %s on %s" %
(type(operator).__name__, _solution))
# apply operator on solution
newSolution = operator.apply(_solution)
# compute fitness of new solution
newSolution.evaluate(self.algo.evaluator)
# compute fitness improvment rate
if self.algo.maximise:
fir = (newSolution.fitness() -
_solution.fitness()) / _solution.fitness()
else:
fir = (_solution.fitness() -
newSolution.fitness()) / _solution.fitness()
if fir > 0:
operator_index = self.operators.index(operator)
self.rewards[operator_index] += fir
self.occurences[operator_index] += 1
logging.info("---- Obtaining %s" % (_solution))
return newSolution