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Add pluggable Threshold-Elites and Dominated Novelty Search (DNS) database variants alongside MAP-Elites #416

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Add pluggable Threshold-Elites and Dominated Novelty Search (DNS) database variants alongside MAP-Elites #416

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3 changes: 3 additions & 0 deletions openevolve/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -352,6 +352,9 @@ class DatabaseConfig:
embedding_model: Optional[str] = None
similarity_threshold: float = 0.99

# Bahlous-Boldi, R., Faldor, M., Grillotti, L., Janmohamed, H., Coiffard, L., Spector, L., & Cully, A. (2025, July). Dominated novelty search: Rethinking local competition in quality-diversity. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 104-112).
variant: str = "DNSProgramDatabase"
distance_threshold: float = 1.5

@dataclass
class EvaluatorConfig:
Expand Down
7 changes: 5 additions & 2 deletions openevolve/controller.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

import importlib
from openevolve.config import Config, load_config
from openevolve.database import Program, ProgramDatabase
from openevolve.evaluator import Evaluator
Expand Down Expand Up @@ -149,7 +149,10 @@ def __init__(
self.config.database.random_seed = self.config.random_seed

self.config.database.novelty_llm = self.llm_ensemble
self.database = ProgramDatabase(self.config.database)
database_cls_name = getattr(self.config.database, "variant", "ProgramDatabase")
database_mod = importlib.import_module("openevolve.database")
database_cls = getattr(database_mod, database_cls_name, None)
self.database = database_cls(self.config.database)

self.evaluator = Evaluator(
self.config.evaluator,
Expand Down
260 changes: 258 additions & 2 deletions openevolve/database.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,8 @@
from openevolve.config import DatabaseConfig
from openevolve.utils.code_utils import calculate_edit_distance
from openevolve.utils.metrics_utils import safe_numeric_average, get_fitness_score
import copy
import math

logger = logging.getLogger(__name__)

Expand Down Expand Up @@ -1205,7 +1207,8 @@ def _update_best_program(self, program: Program) -> None:
self.best_program_id = program.id

# Log the change
if "combined_score" in program.metrics and "combined_score" in current_best.metrics:
if "combined_score" in program.metrics and "combined_score" in current_best.metrics \
and not math.isinf(program.metrics["combined_score"]) and not math.isinf(current_best.metrics["combined_score"]):
old_score = current_best.metrics["combined_score"]
new_score = program.metrics["combined_score"]
score_diff = new_score - old_score
Expand Down Expand Up @@ -1313,6 +1316,7 @@ def _sample_exploration_parent(self) -> Program:
metadata={"island": self.current_island},
artifacts_json=best_program.artifacts_json,
artifact_dir=best_program.artifact_dir,
embedding=copy.deepcopy(best_program.embedding),
)
self.programs[copy_program.id] = copy_program
self.islands[self.current_island].add(copy_program.id)
Expand Down Expand Up @@ -1359,6 +1363,7 @@ def _sample_exploration_parent(self) -> Program:
metadata={"island": self.current_island},
artifacts_json=best_program.artifacts_json,
artifact_dir=best_program.artifact_dir,
embedding=copy.deepcopy(best_program.embedding),
)
self.programs[copy_program.id] = copy_program
self.islands[self.current_island].add(copy_program.id)
Expand Down Expand Up @@ -1983,7 +1988,7 @@ def get_island_stats(self) -> List[dict]:
get_fitness_score(p.metrics, self.config.feature_dimensions)
for p in island_programs
]

scores = [score for score in scores if not math.isinf(score)]
best_score = max(scores) if scores else 0.0
avg_score = sum(scores) / len(scores) if scores else 0.0
diversity = self._calculate_island_diversity(island_programs)
Expand Down Expand Up @@ -2561,3 +2566,254 @@ def log_prompt(
if program_id not in self.prompts_by_program:
self.prompts_by_program[program_id] = {}
self.prompts_by_program[program_id][template_key] = prompt


class ThresholdElitesProgramDatabase(ProgramDatabase):
def add(
self, program: Program, iteration: int = None, target_island: Optional[int] = None
) -> str:
"""
Add a program to the database

Args:
program: Program to add
iteration: Current iteration (defaults to last_iteration)
target_island: Specific island to add to (auto-detects parent's island if None)

Returns:
Program ID
"""
# Store the program
# If iteration is provided, update the program's iteration_found
# Bahlous-Boldi, R., Faldor, M., Grillotti, L., Janmohamed, H., Coiffard, L., Spector, L., & Cully, A. (2025, July). Dominated novelty search: Rethinking local competition in quality-diversity. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 104-112).
if iteration is not None:
program.iteration_found = iteration
# Update last_iteration if needed
self.last_iteration = max(self.last_iteration, iteration)

self.programs[program.id] = program

# Calculate feature coordinates for Threshold-Elites
embd = self._calculate_feature_coords(program)
score = get_fitness_score(program.metrics, self.config.feature_dimensions)
program.embedding = embd
if "combined_score" not in program.metrics:
program.metrics["combined_score"] = score

# Determine target island
# If target_island is not specified and program has a parent, inherit parent's island
if target_island is None and program.parent_id:
parent = self.programs.get(program.parent_id)
if parent and "island" in parent.metadata:
# Child inherits parent's island to maintain island isolation
island_idx = parent.metadata["island"]
logger.debug(
f"Program {program.id} inheriting island {island_idx} from parent {program.parent_id}"
)
else:
# Parent not found or has no island, use current_island
island_idx = self.current_island
if parent:
logger.warning(
f"Parent {program.parent_id} has no island metadata, using current_island {island_idx}"
)
else:
logger.warning(
f"Parent {program.parent_id} not found, using current_island {island_idx}"
)
elif target_island is not None:
# Explicit target island specified (e.g., for migrants)
island_idx = target_island
else:
# No parent and no target specified, use current island
island_idx = self.current_island

island_idx = island_idx % len(self.islands) # Ensure valid island

# Novelty check before adding
if not self._is_novel(program.id, island_idx):
logger.debug(
f"Program {program.id} failed in novelty check and won't be added in the island {island_idx}"
)
return program.id # Do not add non-novel program


distance_threshold = getattr(self.config, "distance_threshold", 1)
for pid in self.islands[island_idx]:
other = self.programs[pid]

if other.embedding is None:
logger.warning(
f"Warning: Program {other.id} has no embedding, skipping similarity check"
)
continue

distance = math.sqrt(sum((a - b) ** 2 for a, b in zip(embd, other.embedding)))

if distance < distance_threshold:
if score > other.metrics["combined_score"]:
other.metrics["combined_score"] = float("-inf")
else:
program.metrics["combined_score"] = float("-inf")


# Add to island
self.islands[island_idx].add(program.id)

# Track which island this program belongs to
program.metadata["island"] = island_idx

# Update archive
self._update_archive(program)

# Enforce population size limit BEFORE updating best program tracking
# This ensures newly added programs aren't immediately removed
self._enforce_population_limit(exclude_program_id=program.id)

# Update the absolute best program tracking (after population enforcement)
self._update_best_program(program)

# Update island-specific best program tracking
self._update_island_best_program(program, island_idx)

# Save to disk if configured
if self.config.db_path:
self._save_program(program)

logger.debug(f"Added program {program.id} to island {island_idx}")

return program.id


class DNSProgramDatabase(ProgramDatabase):
def add(
self, program: Program, iteration: int = None, target_island: Optional[int] = None
) -> str:
"""
Add a program to the database

Args:
program: Program to add
iteration: Current iteration (defaults to last_iteration)
target_island: Specific island to add to (auto-detects parent's island if None)

Returns:
Program ID
"""
# Store the program
# If iteration is provided, update the program's iteration_found
# Bahlous-Boldi, R., Faldor, M., Grillotti, L., Janmohamed, H., Coiffard, L., Spector, L., & Cully, A. (2025, July). Dominated novelty search: Rethinking local competition in quality-diversity. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 104-112).
if iteration is not None:
program.iteration_found = iteration
# Update last_iteration if needed
self.last_iteration = max(self.last_iteration, iteration)

self.programs[program.id] = program

# Calculate feature coordinates for Threshold-Elites
embd = self._calculate_feature_coords(program)
score = get_fitness_score(program.metrics, self.config.feature_dimensions)
program.embedding = embd
if "combined_score" not in program.metrics:
program.metrics["combined_score"] = score

# Determine target island
# If target_island is not specified and program has a parent, inherit parent's island
if target_island is None and program.parent_id:
parent = self.programs.get(program.parent_id)
if parent and "island" in parent.metadata:
# Child inherits parent's island to maintain island isolation
island_idx = parent.metadata["island"]
logger.debug(
f"Program {program.id} inheriting island {island_idx} from parent {program.parent_id}"
)
else:
# Parent not found or has no island, use current_island
island_idx = self.current_island
if parent:
logger.warning(
f"Parent {program.parent_id} has no island metadata, using current_island {island_idx}"
)
else:
logger.warning(
f"Parent {program.parent_id} not found, using current_island {island_idx}"
)
elif target_island is not None:
# Explicit target island specified (e.g., for migrants)
island_idx = target_island
else:
# No parent and no target specified, use current island
island_idx = self.current_island

island_idx = island_idx % len(self.islands) # Ensure valid island

# Novelty check before adding
if not self._is_novel(program.id, island_idx):
logger.debug(
f"Program {program.id} failed in novelty check and won't be added in the island {island_idx}"
)
return program.id # Do not add non-novel program

def _is_negative_infinity(value):
return math.isinf(value) and value < 0

p2dns = dict()
pids = list(self.islands[island_idx]) + [program.id]

for pid_i in pids:
program_i = self.programs[pid_i]

if _is_negative_infinity(program_i.metrics["combined_score"]):
p2dns[pid_i] = float("-inf")
continue

D_i = []
for pid_j in pids:
if pid_j != pid_i:
program_j = self.programs[pid_j]
if program_j.metrics["combined_score"] > program_i.metrics["combined_score"]:
D_i.append(pid_j)

if len(D_i) > 0:
distances = []
for pid_j in D_i:
program_j = self.programs[pid_j]
distances.append(math.sqrt(sum((a - b) ** 2 for a, b in zip(program_i.embedding, program_j.embedding))))
dominated_novelty_score = np.mean(distances)
else:
dominated_novelty_score = float("+inf")
p2dns[pid_i] = dominated_novelty_score

dns_median = np.median(list(p2dns.values()))
for pid_i in pids:
program_i = self.programs[pid_i]
dominated_novelty_score = p2dns[pid_i]
if dominated_novelty_score < dns_median:
program_i.metrics["combined_score"] = float("-inf")

# Add to island
self.islands[island_idx].add(program.id)

# Track which island this program belongs to
program.metadata["island"] = island_idx

# Update archive
self._update_archive(program)

# Enforce population size limit BEFORE updating best program tracking
# This ensures newly added programs aren't immediately removed
self._enforce_population_limit(exclude_program_id=program.id)

# Update the absolute best program tracking (after population enforcement)
self._update_best_program(program)

# Update island-specific best program tracking
self._update_island_best_program(program, island_idx)

# Save to disk if configured
if self.config.db_path:
self._save_program(program)

logger.debug(f"Added program {program.id} to island {island_idx}")

return program.id
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