def mut_shrink(individual: gp.PrimitiveTree,
rng: random.Random) -> Tuple[gp.PrimitiveTree]:
"""Replace node with one of its arguments"""
# We don't want to "shrink" the root
if len(individual) < 3 or individual.height <= 1:
return individual,
iprims = []
for i, node in enumerate(individual[1:], 1):
if isinstance(node, gp.Primitive) and node.ret in node.args:
iprims.append((i, node))
if len(iprims) != 0:
index, prim = rng.choice(iprims)
arg_idx = rng.choice(
[i for i, type_ in enumerate(prim.args) if type_ == prim.ret])
rindex = index + 1
for _ in range(arg_idx + 1):
rslice = individual.searchSubtree(rindex)
subtree = individual[rslice]
rindex += len(subtree)
slice_ = individual.searchSubtree(index)
individual[slice_] = subtree
return individual,
def cx_one_point(
ind1: gp.PrimitiveTree, ind2: gp.PrimitiveTree,
rng: random.Random) -> Tuple[gp.PrimitiveTree, gp.PrimitiveTree]:
if len(ind1) < 2 or len(ind2) < 2:
# No crossover on single node tree
return ind1, ind2
# List all available primitive types in each individual
types1 = collections.defaultdict(list)
types2 = collections.defaultdict(list)
for idx, node in enumerate(ind1[1:], 1):
types1[node.ret].append(idx)
for idx, node in enumerate(ind2[1:], 1):
types2[node.ret].append(idx)
common_types = set(types1.keys()).intersection(set(types2.keys()))
if len(common_types) > 0:
type_ = rng.choice(list(common_types))
index1 = rng.choice(types1[type_])
index2 = rng.choice(types2[type_])
slice1 = ind1.searchSubtree(index1)
slice2 = ind2.searchSubtree(index2)
ind1[slice1], ind2[slice2] = ind2[slice2], ind1[slice1]
return ind1, ind2
def mut_uniform(individual: gp.PrimitiveTree, expr: Callable,
pset: gp.PrimitiveSetTyped, rng: random.Random):
"""Replace subtree with randomly generated tree"""
index = rng.randrange(len(individual))
slice_ = individual.searchSubtree(index)
type_ = individual[index].ret
individual[slice_] = expr(pset=pset, type_=type_)
return individual,