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# evo_architecture.py
import random
import json
import os
import uuid
import csv
GENOME_LOG = "genome_log.csv"
BEST_GENOME_FILE = "best_genome.json"
# Mutation bounds
MUTATION_LIMITS = {
"num_layers": (2, 12),
"ffn_dim": (256, 4096),
"num_heads": (2, 16),
}
def default_config():
return {
"genome_id": str(uuid.uuid4()),
"num_layers": 6,
"ffn_dim": 1024,
"num_heads": 8,
"memory_enabled": True
}
def mutate_genome(base_config, exploration_rate=0.5):
"""Mutates the genome. Exploration rate controls how wild the mutation is (0 = minor, 1 = wild)."""
config = base_config.copy()
config["genome_id"] = str(uuid.uuid4())
# Select mutation type
mutation_type = random.choice(["num_layers", "ffn_dim", "num_heads", "memory_enabled"])
if mutation_type == "memory_enabled":
config["memory_enabled"] = not config["memory_enabled"]
else:
min_val, max_val = MUTATION_LIMITS[mutation_type]
change = int((max_val - min_val) * exploration_rate)
delta = random.randint(-change, change)
config[mutation_type] = max(min_val, min(max_val, config[mutation_type] + delta))
return config
def log_genome(config, score=None):
"""Logs genome config + performance to CSV."""
row = [
config.get("genome_id", ""),
config["num_layers"],
config["ffn_dim"],
config["num_heads"],
config["memory_enabled"],
score if score is not None else ""
]
file_exists = os.path.exists(GENOME_LOG)
with open(GENOME_LOG, "a", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
if not file_exists:
writer.writerow(["genome_id", "num_layers", "ffn_dim", "num_heads", "memory_enabled", "score"])
writer.writerow(row)
def save_best_genome(config):
"""Saves the best-performing genome to file."""
with open(BEST_GENOME_FILE, "w", encoding="utf-8") as f:
json.dump(config, f)
def load_best_genome():
"""Loads best-performing genome (if exists), else default."""
if os.path.exists(BEST_GENOME_FILE):
with open(BEST_GENOME_FILE, "r", encoding="utf-8") as f:
return json.load(f)
return default_config()
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