Files
tensorneat-mend/tensorneat/algorithm/neat/ga/crossover/default.py

69 lines
2.4 KiB
Python

import jax, jax.numpy as jnp
from .base import BaseCrossover
class DefaultCrossover(BaseCrossover):
def __call__(self, state, randkey, genome, nodes1, conns1, nodes2, conns2):
"""
use genome1 and genome2 to generate a new genome
notice that genome1 should have higher fitness than genome2 (genome1 is winner!)
"""
randkey1, randkey2 = jax.random.split(randkey, 2)
randkeys1 = jax.random.split(randkey1, genome.max_nodes)
randkeys2 = jax.random.split(randkey2, genome.max_conns)
# crossover nodes
keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
# make homologous genes align in nodes2 align with nodes1
nodes2 = self.align_array(keys1, keys2, nodes2, is_conn=False)
# For not homologous genes, use the value of nodes1(winner)
# For homologous genes, use the crossover result between nodes1 and nodes2
new_nodes = jnp.where(
jnp.isnan(nodes1) | jnp.isnan(nodes2),
nodes1,
jax.vmap(genome.node_gene.crossover, in_axes=(None, 0, 0, 0))(state, randkeys1, nodes1, nodes2),
)
# crossover connections
con_keys1, con_keys2 = conns1[:, :2], conns2[:, :2]
conns2 = self.align_array(con_keys1, con_keys2, conns2, is_conn=True)
new_conns = jnp.where(
jnp.isnan(conns1) | jnp.isnan(conns2),
conns1,
jax.vmap(genome.conn_gene.crossover, in_axes=(None, 0, 0, 0))(state, randkeys2, conns1, conns2),
)
return new_nodes, new_conns
def align_array(self, seq1, seq2, ar2, is_conn: bool):
"""
After I review this code, I found that it is the most difficult part of the code.
Please consider carefully before change it!
make ar2 align with ar1.
:param seq1:
:param seq2:
:param ar2:
:param is_conn:
:return:
align means to intersect part of ar2 will be at the same position as ar1,
non-intersect part of ar2 will be set to Nan
"""
seq1, seq2 = seq1[:, jnp.newaxis], seq2[jnp.newaxis, :]
mask = (seq1 == seq2) & (~jnp.isnan(seq1))
if is_conn:
mask = jnp.all(mask, axis=2)
intersect_mask = mask.any(axis=1)
idx = jnp.arange(0, len(seq1))
idx_fixed = jnp.dot(mask, idx)
refactor_ar2 = jnp.where(
intersect_mask[:, jnp.newaxis], ar2[idx_fixed], jnp.nan
)
return refactor_ar2