def _expander(fringe, iteration, viewer):
fitness = [x.value for x in fringe]
sampler = InverseTransformSampler(fitness, fringe)
new_generation = []
expanded_nodes = []
expanded_neighbors = []
for _ in fringe:
node1 = sampler.sample()
node2 = sampler.sample()
child = problem.crossover(node1.state, node2.state)
action = 'crossover'
if random.random() < mutation_chance:
# Noooouuu! she is... he is... *IT* is a mutant!
child = problem.mutate(child)
action += '+mutation'
child_node = SearchNodeValueOrdered(state=child,
problem=problem,
action=action)
new_generation.append(child_node)
expanded_nodes.append(node1)
expanded_neighbors.append([child_node])
expanded_nodes.append(node2)
expanded_neighbors.append([child_node])
if viewer:
viewer.event('expanded', expanded_nodes, expanded_neighbors)
fringe.clear()
for node in new_generation:
fringe.append(node)
def _expander(fringe, iteration, viewer):
fitness = [x.value for x in fringe]
sampler = InverseTransformSampler(fitness, fringe)
new_generation = []
expanded_nodes = []
expanded_neighbors = []
for _ in fringe:
node1 = sampler.sample()
node2 = sampler.sample()
child = problem.crossover(node1.state, node2.state)
action = 'crossover'
if random.random() < mutation_chance:
# Noooouuu! she is... he is... *IT* is a mutant!
child = problem.mutate(child)
action += '+mutation'
child_node = SearchNodeValueOrdered(state=child, problem=problem, action=action)
new_generation.append(child_node)
expanded_nodes.append(node1)
expanded_neighbors.append([child_node])
expanded_nodes.append(node2)
expanded_neighbors.append([child_node])
if viewer:
viewer.event('expanded', expanded_nodes, expanded_neighbors)
fringe.clear()
for node in new_generation:
fringe.append(node)
def _expander(fringe, iteration):
fitness = [x.value for x in fringe]
sampler = InverseTransformSampler(fitness, fringe)
new_generation = []
for _ in fringe:
node1 = sampler.sample()
node2 = sampler.sample()
child = problem.crossover(node1.state, node2.state)
if random.random() < mutation_chance:
# Noooouuu! she is... he is... *IT* is a mutant!
child = problem.mutate(child)
new_generation.append(child)
fringe.clear()
for s in new_generation:
fringe.append(SearchNodeValueOrdered(state=s, problem=problem))
def _expander(fringe, iteration):
fitness = [x.value() for x in fringe]
sampler = InverseTransformSampler(fitness, fringe)
new_generation = []
for _ in fringe:
node1 = sampler.sample()
node2 = sampler.sample()
child = problem.crossover(node1.state, node2.state)
if random.random() < mutation_chance:
# Noooouuu! she is... he is... *IT* is a mutant!
child = problem.mutate(child)
new_generation.append(child)
fringe.clear()
for s in new_generation:
fringe.append(SearchNodeValueOrdered(state=s, problem=problem))
