def LTGE_crossover(p_0, p_1):
"""Crossover in the LTGE representation."""
# crossover and repair.
# the LTGE crossover produces one child, and is symmetric (ie
# xover(p0, p1) is not different from xover(p1, p0)), but since it's
# stochastic we can just run it twice to get two individuals
# expected to be different.
g_0, ph_0 = latent_tree_repair(
latent_tree_crossover(p_0.genome, p_1.genome), params['BNF_GRAMMAR'],
params['MAX_TREE_DEPTH'])
g_1, ph_1 = latent_tree_repair(
latent_tree_crossover(p_0.genome, p_1.genome), params['BNF_GRAMMAR'],
params['MAX_TREE_DEPTH'])
# wrap up in Individuals and fix up various Individual attributes
ind_0 = individual.Individual(g_0, None, False)
ind_1 = individual.Individual(g_1, None, False)
ind_0.phenotype = ph_0
ind_1.phenotype = ph_1
# number of nodes is the number of decisions in the genome
ind_0.nodes = ind_0.used_codons = len(g_0)
ind_1.nodes = ind_1.used_codons = len(g_1)
# each key is the length of a path from root
ind_0.depth = max(len(k) for k in g_0)
ind_1.depth = max(len(k) for k in g_1)
# in LTGE there are no invalid individuals
ind_0.invalid = False
ind_1.invalid = False
return [ind_0, ind_1]
def LTGE_crossover(p_0, p_1):
"""Crossover in the LTGE representation."""
# crossover and repair.
# the LTGE crossover produces one child, and is symmetric (ie
# xover(p0, p1) is not different from xover(p1, p0)), but since it's
# stochastic we can just run it twice to get two individuals
# expected to be different.
g_0, ph_0 = latent_tree_repair(
latent_tree_crossover(p_0.genome, p_1.genome),
params['BNF_GRAMMAR'], params['MAX_TREE_DEPTH'])
g_1, ph_1 = latent_tree_repair(
latent_tree_crossover(p_0.genome, p_1.genome),
params['BNF_GRAMMAR'], params['MAX_TREE_DEPTH'])
# wrap up in Individuals and fix up various Individual attributes
ind_0 = individual.Individual(g_0, None, False)
ind_1 = individual.Individual(g_1, None, False)
ind_0.phenotype = ph_0
ind_1.phenotype = ph_1
# number of nodes is the number of decisions in the genome
ind_0.nodes = ind_0.used_codons = len(g_0)
ind_1.nodes = ind_1.used_codons = len(g_1)
# each key is the length of a path from root
ind_0.depth = max(len(k) for k in g_0)
ind_1.depth = max(len(k) for k in g_1)
# in LTGE there are no invalid individuals
ind_0.invalid = False
ind_1.invalid = False
return [ind_0, ind_1]
def LTGE_mutation(ind):
"""Mutation in the LTGE representation."""
# mutate and repair.
g, ph = latent_tree_repair(latent_tree_mutate(ind.genome),
params['BNF_GRAMMAR'], params['MAX_TREE_DEPTH'])
# wrap up in an Individual and fix up various Individual attributes
ind = individual.Individual(g, None, False)
ind.phenotype = ph
# number of nodes is the number of decisions in the genome
ind.nodes = ind.used_codons = len(g)
# each key is the length of a path from root
ind.depth = max(len(k) for k in g)
# in LTGE there are no invalid individuals
ind.invalid = False
return ind
def LTGE_mutation(ind):
"""Mutation in the LTGE representation."""
# mutate and repair.
g, ph = latent_tree_repair(latent_tree_mutate(ind.genome),
params['BNF_GRAMMAR'], params['MAX_TREE_DEPTH'])
# wrap up in an Individual and fix up various Individual attributes
ind = individual.Individual(g, None, False)
ind.phenotype = ph
# number of nodes is the number of decisions in the genome
ind.nodes = ind.used_codons = len(g)
# each key is the length of a path from root
ind.depth = max(len(k) for k in g)
# in LTGE there are no invalid individuals
ind.invalid = False
return ind