def mate(self, parentOne, parentTwo):
"""Accepts two parents and returns ONE offspring; if the offspring is unchanged from one of
the parents, the fitness values are not re-evaluated, just copied. Only one category of
mutation is performed on a single offspring."""
# one parent will form the basis for the new offspring; the other is just there for
# potential crossover (by default the mother will become the offspring)
mother, father = parentOne.copy(), parentTwo.copy()
# fitEval will be set to True if any mutations occur that make the offspring different from
# its copied parent; this way we avoid unnecessary fitness evaluations
fitEval = False
# only one category of mutation is allowed per mating event
r = urand()
if r < self.pC: # parental crossover
fitEval = True
mNode = rchoice(mother.tree.getNodes())
fNode = rchoice(father.tree.getNodes())
CGAGenerator.single_crossover(mNode,fNode)
elif r < self.pC + self.pHC: # headless chicken crossover
fitEval = True
mNode = rchoice(mother.tree.getNodes())
rNode = rchoice(self.initialize_tree().getNodes())
CGAGenerator.single_crossover(mNode,rNode)
elif r < self.pC + self.pHC + self.pM: # point mutation (uses pM/node for mutation prob.)
fitEval = True
for n in mother.tree.getNodes():
if urand() < self.pM:
CGAGenerator.point_mutate(mother.tree, n)
fitEval = True
elif r < self.pC + self.pHC + self.pM + self.pP: # pruning - guaranteed to do one pruning operation
fitEval = True
mNode = rchoice(mother.tree.getNodes())
CGAGenerator.prune(mother.tree,mNode)
elif r < self.pC + self.pHC + self.pM + self.pP + self.pG: # growth - guaranteed to do one growth op.
fitEval = True
mTerm = rchoice(mother.tree.getTermini())
CGAGenerator.grow(mother.tree,mTerm)
else: # offspring will just be a copy
pass
if fitEval:
mother.fitness,mother.parsimony,mother.finitewts = self.evaluate_fitness(mother.tree)
return mother
