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AETHER Evolution Engine.
Integrates AlphaEvolve-style code diff evolution,
GEA-style group experience sharing,
MAP-Elites diversity maintenance,
and HiMAC hierarchical co-evolution phases.
"""
import numpy as np
import torch
from typing import List, Dict, Any, Callable, Optional, Tuple
import random
import copy
import logging
from dataclasses import dataclass, fields
logger = logging.getLogger("AETHER.Evolution")
@dataclass
class ArchitectureDNA:
"""Genotype encoding for AETHER architecture variants."""
population_size: int
mutation_rate: float
learning_rate: float
macro_policy_dim: int
micro_policy_dim: int
num_agents: int
kg_embedding_dim: int
symbolic_bias: float = 0.5 # 0=neural, 1=symbolic
def to_vector(self) -> np.ndarray:
return np.array([
self.population_size,
self.mutation_rate,
self.learning_rate * 1e5, # scale for numerical stability
self.macro_policy_dim,
self.micro_policy_dim,
self.num_agents,
self.kg_embedding_dim,
self.symbolic_bias * 10,
])
@classmethod
def from_vector(cls, vec: np.ndarray) -> "ArchitectureDNA":
return cls(
population_size=int(np.clip(vec[0], 2, 64)),
mutation_rate=float(np.clip(vec[1], 0.01, 0.5)),
learning_rate=float(np.clip(vec[2] / 1e5, 1e-6, 1e-3)),
macro_policy_dim=int(np.clip(vec[3], 64, 512)),
micro_policy_dim=int(np.clip(vec[4], 32, 256)),
num_agents=int(np.clip(vec[5], 1, 16)),
kg_embedding_dim=int(np.clip(vec[6], 32, 512)),
symbolic_bias=float(np.clip(vec[7] / 10, 0.0, 1.0)),
)
def to_config_dict(self) -> Dict[str, Any]:
return {
"population_size": self.population_size,
"mutation_rate": self.mutation_rate,
"learning_rate": self.learning_rate,
"macro_policy_dim": self.macro_policy_dim,
"micro_policy_dim": self.micro_policy_dim,
"num_agents": self.num_agents,
"kg_embedding_dim": self.kg_embedding_dim,
}
class MAPelitesArchive:
"""
MAP-Elites archive for quality-diversity optimization.
Cells indexed by behavioral descriptors (capability dimensions).
"""
def __init__(self, dims: Tuple[int, int] = (10, 10),
ranges: List[Tuple[float, float]] = None):
self.dims = dims
self.ranges = ranges or [(0, 1), (0, 1)]
self.archive: Dict[Tuple[int, int], Tuple[ArchitectureDNA, float]] = {}
def _get_index(self, measures: np.ndarray) -> Tuple[int, int]:
"""Map continuous measures to discrete cell indices."""
indices = []
for m, (low, high), dim in zip(measures, self.ranges, self.dims):
normalized = (m - low) / (high - low + 1e-8)
idx = int(np.clip(normalized * dim, 0, dim - 1))
indices.append(idx)
return tuple(indices)
def add(self, dna: ArchitectureDNA, fitness: float,
measures: np.ndarray) -> bool:
"""Add solution to archive. Returns True if improved cell."""
idx = self._get_index(measures)
if idx not in self.archive or self.archive[idx][1] < fitness:
self.archive[idx] = (dna, fitness)
return True
return False
def sample(self, n: int = 1) -> List[ArchitectureDNA]:
"""Sample random solutions from archive."""
if not self.archive:
return []
items = list(self.archive.values())
selected = random.sample(items, min(n, len(items)))
return [dna for dna, _ in selected]
def get_best(self) -> Optional[Tuple[ArchitectureDNA, float]]:
"""Get highest fitness solution."""
if not self.archive:
return None
return max(self.archive.values(), key=lambda x: x[1])
def stats(self) -> Dict[str, float]:
total_cells = self.dims[0] * self.dims[1]
return {
"coverage": len(self.archive) / total_cells,
"qd_score": sum(f for _, f in self.archive.values()),
"max_fitness": max((f for _, f in self.archive.values()), default=0),
}
class AetherEvolutionEngine:
"""
Evolutionary engine combining:
- AlphaEvolve-style LLM-guided mutation (code diffs)
- GEA-style group experience sharing
- MAP-Elites quality-diversity
- HiMAC hierarchical co-evolution phases
"""
def __init__(self, config):
self.config = config
self.archive = MAPelitesArchive(
dims=(10, 10),
ranges=[(0, 1), (0, 1)], # (symbolic_bias, task_complexity)
)
self.generation = 0
self.experience_log: List[Dict] = [] # GEA experience sharing
def generate_candidates(self, base_config,
population_size: int = 8) -> List[Any]:
"""
Generate candidate architecture variants.
Uses mutation + archive seeding.
"""
candidates = []
# Seed from archive if available
archive_seeds = self.archive.sample(n=min(2, len(self.archive.archive)))
# Always include base config
candidates.append(base_config)
# Mutate base config
for _ in range(population_size - len(archive_seeds) - 1):
mutated = self._mutate_config(base_config)
candidates.append(mutated)
# Add archive seeds (converted back to config format)
for dna in archive_seeds:
from .core import AetherConfig
cfg = AetherConfig(**dna.to_config_dict())
candidates.append(cfg)
return candidates
def _mutate_config(self, config) -> Any:
"""Apply constrained mutation to config."""
from .core import AetherConfig
dna = ArchitectureDNA(
population_size=config.population_size,
mutation_rate=config.mutation_rate,
learning_rate=config.learning_rate,
macro_policy_dim=config.macro_policy_dim,
micro_policy_dim=config.micro_policy_dim,
num_agents=config.num_agents,
kg_embedding_dim=config.kg_embedding_dim,
symbolic_bias=getattr(config, 'symbolic_bias', 0.5),
)
vec = dna.to_vector()
# Gaussian mutation (AlphaEvolve-style: small perturbations)
noise = np.random.normal(0, config.mutation_rate, size=vec.shape)
mutated_vec = vec + noise * vec # proportional mutation
new_dna = ArchitectureDNA.from_vector(mutated_vec)
new_config = AetherConfig(**new_dna.to_config_dict())
new_config.generations = config.generations
new_config.sandbox_timeout = config.sandbox_timeout
new_config.max_architecture_depth = config.max_architecture_depth
new_config.enable_self_modification = config.enable_self_modification
return new_config
def select(self, candidates: List[Any], fitness_scores: List[float],
alpha_exploration: float = 0.3) -> List[Any]:
"""
Select candidates using Performance-Novelty scoring (from GEA).
score(i) = performance_i * sqrt(novelty_i)
"""
if not candidates or not fitness_scores:
return candidates[:2] if len(candidates) >= 2 else candidates
vectors = []
for cfg in candidates:
dna = ArchitectureDNA(
population_size=cfg.population_size,
mutation_rate=cfg.mutation_rate,
learning_rate=cfg.learning_rate,
macro_policy_dim=cfg.macro_policy_dim,
micro_policy_dim=cfg.micro_policy_dim,
num_agents=cfg.num_agents,
kg_embedding_dim=cfg.kg_embedding_dim,
)
vectors.append(dna.to_vector())
vectors = np.array(vectors)
f = np.array(fitness_scores)
f_norm = (f - f.min()) / (f.max() - f.min() + 1e-8)
k = min(4, len(candidates) - 1)
novelties = []
for i, v in enumerate(vectors):
distances = np.linalg.norm(vectors - v, axis=1)
distances[i] = np.inf # exclude self
knn = np.partition(distances, k)[:k]
novelty = np.mean(knn)
novelties.append(novelty)
novelties = np.array(novelties)
nov_norm = novelties / (novelties.max() + 1e-8)
scores = f_norm * np.sqrt(nov_norm + 1e-8)
n_select = max(1, len(candidates) // 2)
top_indices = np.argsort(scores)[-n_select:]
selected = [candidates[i] for i in top_indices]
logger.info(f"Selected {len(selected)} candidates. "
f"Score range: [{scores.min():.3f}, {scores.max():.3f}]")
return selected
def mutate(self, candidates: List[Any], mutation_rate: float = 0.15,
max_depth: int = 5) -> List[Any]:
"""
Apply constrained mutations.
Enforces max architecture depth and safety constraints.
"""
mutated = []
for cfg in candidates:
new_cfg = self._mutate_config(cfg)
if new_cfg.macro_policy_dim > 512:
new_cfg.macro_policy_dim = 512
if new_cfg.micro_policy_dim > new_cfg.macro_policy_dim:
new_cfg.micro_policy_dim = new_cfg.macro_policy_dim // 2
if new_cfg.num_agents > max_depth * 2:
new_cfg.num_agents = max_depth * 2
mutated.append(new_cfg)
return mutated
def co_evolve_phases(self, macro_policy, micro_policy,
macro_env_fn, micro_env_fn,
num_iterations: int = 10) -> Tuple[Any, Any]:
"""
HiMAC-style iterative co-evolution.
Phase A: Macro-exploration (freeze micro)
Phase B: Micro-adaptation (freeze macro, train on best blueprint)
"""
logger.info(f"Starting hierarchical co-evolution for {num_iterations} iterations")
best_blueprint = None
best_reward = -float('inf')
for iteration in range(num_iterations):
logger.info(f"Iteration {iteration}: Phase A - Macro Exploration")
blueprints = []
rewards = []
for _ in range(8):
blueprint = macro_policy.sample()
reward = macro_env_fn(blueprint, micro_policy)
blueprints.append(blueprint)
rewards.append(reward)
r = np.array(rewards)
advantages = (r - r.mean()) / (r.std() + 1e-8)
macro_policy.update(blueprints, advantages)
best_idx = int(np.argmax(rewards))
if rewards[best_idx] > best_reward:
best_reward = rewards[best_idx]
best_blueprint = blueprints[best_idx]
logger.info(f"Iteration {iteration}: Phase B - Micro Adaptation")
if best_blueprint is not None:
micro_policy.update(best_blueprint, micro_env_fn)
return macro_policy, micro_policy
def share_experience(self, agent_group: List[Any],
traces: List[Dict]) -> List[str]:
"""
GEA-style experience sharing: agents reflect on group traces
and generate evolution directives.
"""
aggregated = {
"patches_applied": [],
"predicted_patches": [],
"execution_logs": [],
"outcomes": [],
}
for trace in traces:
for key in aggregated:
if key in trace:
aggregated[key].append(trace[key])
directives = []
for agent in agent_group:
directive = self._generate_directive(agent, aggregated)
directives.append(directive)
self.experience_log.append({
"generation": self.generation,
"group_size": len(agent_group),
"traces": len(traces),
"directives": directives,
})
self.generation += 1
return directives
def _generate_directive(self, agent, aggregated: Dict) -> str:
success_rate = (np.mean(aggregated["outcomes"])
if aggregated["outcomes"] else 0.5)
if success_rate < 0.3:
return "Increase exploration diversity. Decrease learning rate. Add more agents."
elif success_rate > 0.8:
return "Consolidate current strategy. Increase exploitation. Optimize inference speed."
else:
return "Balance exploration and exploitation. Refine tool definitions."
def update_archive(self, candidates: List[Any],
fitness_scores: List[float]) -> None:
"""Update MAP-Elites archive with evaluated candidates."""
for cfg, fitness in zip(candidates, fitness_scores):
if fitness == -float('inf'):
continue
symbolic_bias = getattr(cfg, 'symbolic_bias', 0.5)
measures = np.array([
symbolic_bias,
np.clip(fitness, 0, 1),
])
dna = ArchitectureDNA(
population_size=cfg.population_size,
mutation_rate=cfg.mutation_rate,
learning_rate=cfg.learning_rate,
macro_policy_dim=cfg.macro_policy_dim,
micro_policy_dim=cfg.micro_policy_dim,
num_agents=cfg.num_agents,
kg_embedding_dim=cfg.kg_embedding_dim,
symbolic_bias=symbolic_bias,
)
improved = self.archive.add(dna, fitness, measures)
if improved:
logger.debug(f"Archive improved at cell with fitness {fitness:.4f}")
def get_diversity_stats(self) -> Dict[str, float]:
return self.archive.stats()
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