File size: 4,240 Bytes
2facf1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | #!/usr/bin/env python3
from shinka.core import EvolutionRunner, EvolutionConfig
from shinka.database import DatabaseConfig
from shinka.launch import LocalJobConfig
job_config = LocalJobConfig(eval_program_path="evaluate.py")
strategy = "weighted"
if strategy == "uniform":
# 1. Uniform from correct programs
parent_config = dict(
parent_selection_strategy="power_law",
exploitation_alpha=0.0,
exploitation_ratio=1.0,
)
elif strategy == "hill_climbing":
# 2. Hill Climbing (Always from the Best)
parent_config = dict(
parent_selection_strategy="power_law",
exploitation_alpha=100.0,
exploitation_ratio=1.0,
)
elif strategy == "weighted":
# 3. Weighted Prioritization
parent_config = dict(
parent_selection_strategy="weighted",
parent_selection_lambda=10.0,
)
elif strategy == "power_law":
# 4. Power-Law Prioritization
parent_config = dict(
parent_selection_strategy="power_law",
exploitation_alpha=1.0,
exploitation_ratio=0.2,
)
elif strategy == "power_law_high":
# 4. Power-Law Prioritization
parent_config = dict(
parent_selection_strategy="power_law",
exploitation_alpha=2.0,
exploitation_ratio=0.2,
)
elif strategy == "beam_search":
# 5. Beam Search
parent_config = dict(
parent_selection_strategy="beam_search",
num_beams=10,
)
db_config = DatabaseConfig(
db_path="evolution_db.sqlite",
num_islands=2,
archive_size=40,
# Inspiration parameters
elite_selection_ratio=0.3,
num_archive_inspirations=4,
num_top_k_inspirations=2,
# Island migration parameters
migration_interval=10,
migration_rate=0.1, # chance to migrate program to random island
island_elitism=True, # Island elite is protected from migration
**parent_config,
)
search_task_sys_msg = """You are an expert mathematician specializing in circle packing problems and computational geometry. The best known result for the sum of radii when packing 26 circles in a unit square is 2.635.
Key directions to explore:
1. The optimal arrangement likely involves variable-sized circles
2. A pure hexagonal arrangement may not be optimal due to edge effects
3. The densest known circle packings often use a hybrid approach
4. The optimization routine is critically important - simple physics-based models with carefully tuned parameters
5. Consider strategic placement of circles at square corners and edges
6. Adjusting the pattern to place larger circles at the center and smaller at the edges
7. The math literature suggests special arrangements for specific values of n
8. You can use the scipy optimize package (e.g. LP or SLSQP) to optimize the radii given center locations and constraints
Make sure that all circles are disjoint and lie inside the unit square.
Be creative and try to find a new solution better than the best known result."""
evo_config = EvolutionConfig(
task_sys_msg=search_task_sys_msg,
patch_types=["diff", "full", "cross"],
patch_type_probs=[0.6, 0.3, 0.1],
num_generations=200,
max_parallel_jobs=5,
max_patch_resamples=3,
max_patch_attempts=3,
job_type="local",
language="python",
llm_models=[
"native-gemini-3-flash-preview",
],
llm_kwargs=dict(
temperatures=[0.0, 0.5, 1.0],
reasoning_efforts=["auto", "low", "medium", "high"],
max_tokens=65536,
),
meta_rec_interval=10,
meta_llm_models=["native-gemini-3-flash-preview"],
meta_llm_kwargs=dict(temperatures=[0.0], max_tokens=32768),
embedding_model="text-embedding-3-small",
code_embed_sim_threshold=0.995,
novelty_llm_models=["native-gemini-3-flash-preview"],
novelty_llm_kwargs=dict(temperatures=[0.0], max_tokens=32768),
llm_dynamic_selection="ucb1",
llm_dynamic_selection_kwargs=dict(exploration_coef=1.0),
init_program_path="initial.py",
results_dir="results_cpack",
)
def main():
evo_runner = EvolutionRunner(
evo_config=evo_config,
job_config=job_config,
db_config=db_config,
verbose=True,
)
evo_runner.run()
if __name__ == "__main__":
results_data = main() |