Genesis: Towards Open-Ended and Sample-Efficient Program Evolution 🧬

Genesis is a framework that combines Large Language Models (LLMs) with evolutionary algorithms to drive scientific discovery. By leveraging the creative capabilities of LLMs and the optimization power of evolutionary search, Genesis enables automated exploration and improvement of scientific code.

Note: This implementation is based on and extends Shinka AI, an open-source platform for LLM-driven code evolution. We are grateful to the original authors for their foundational work.

The system is inspired by the AI Scientist, AlphaEvolve, and Darwin Goedel Machine. It maintains a population of programs that evolve over generations, with an ensemble of LLMs acting as intelligent mutation operators that suggest code improvements.

The framework supports parallel evaluation of candidates locally, on a Slurm cluster, or in cloud sandboxes. It maintains an archive of successful solutions, enabling knowledge transfer between different evolutionary islands. Genesis is particularly well-suited for scientific tasks where there is a verifier available and the goal is to optimize performance metrics while maintaining code correctness and readability.

Documentation 📝

Guide	Description	What You'll Learn
🚀 Getting Started	Installation, basic usage, and examples	Setup, first evolution run, core concepts
📓 Tutorial Notebook	Interactive walkthrough of Genesis features	Hands-on examples, configuration, best practices
🛠️ Creating Tasks	Guide to creating custom tasks	File structure, evaluation scripts, configuration
☁️ E2B Integration	Running evaluations in cloud sandboxes	Setup, configuration, dependencies
⚙️ Configuration	Comprehensive configuration reference	All config options, optimization settings, advanced features
🎨 WebUI	Interactive visualization and monitoring	Real-time tracking, result analysis, debugging tools
🗺️ Roadmap	Future plans and language support	Supported languages, execution backends, planned features

Installation & Quick Start 🚀

# Clone the repository
git clone https://github.com/GeorgePearse/Genesis
# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create environment and install Genesis
cd Genesis
uv venv --python 3.12
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .

# Run your first evolution experiment
genesis_launch variant=circle_packing_example

For detailed installation instructions and usage examples, see the Getting Started Guide.

Examples 📖

Example	Description	Environment Setup
⭕ Circle Packing	Optimize circle packing to maximize radii.	LocalJobConfig
🤖 Agent Design	Design agent scaffolds for math tasks.	LocalJobConfig
🎯 ALE-Bench	Code optimization for ALE-Bench tasks.	LocalJobConfig
✨ Novelty Generator	Generate creative, surprising outputs (e.g., ASCII art).	LocalJobConfig

genesis Run with Python API 🐍

For the simplest setup with default settings, you only need to specify the evaluation program:

from genesis.core import EvolutionRunner, EvolutionConfig
from genesis.database import DatabaseConfig
from genesis.launch import LocalJobConfig

# Minimal config - only specify what's required
job_config = LocalJobConfig(eval_program_path="evaluate.py")
db_config = DatabaseConfig()
evo_config = EvolutionConfig(init_program_path="initial.py",)

# Run evolution with defaults
runner = EvolutionRunner(
    evo_config=evo_config,
    job_config=job_config,
    db_config=db_config,
)
runner.run()

EvolutionConfig Parameters (click to expand)

Key	Default Value	Type	Explanation
task_sys_msg	None	Optional[str]	System message describing the optimization task
patch_types	["diff"]	List[str]	Types of patches to generate: "diff", "full", "cross"
patch_type_probs	[1.0]	List[float]	Probabilities for each patch type
num_generations	10	int	Number of evolution generations to run
max_parallel_jobs	2	int	Maximum number of parallel evaluation jobs
max_patch_resamples	3	int	Max times to resample a patch if it fails
max_patch_attempts	5	int	Max attempts to generate a valid patch
job_type	"local"	str	Job execution type: "local", "slurm_docker", "slurm_conda"
language	"python"	str	Programming language for evolution
llm_models	["azure-gpt-4.1-mini"]	List[str]	List of LLM models for code generation
llm_dynamic_selection	None	Optional[Union[str, BanditBase]]	Dynamic model selection strategy
llm_dynamic_selection_kwargs	{}	dict	Kwargs for dynamic selection
llm_kwargs	{}	dict	Additional kwargs for LLM calls
meta_rec_interval	None	Optional[int]	Interval for meta-recommendations
meta_llm_models	None	Optional[List[str]]	LLM models for meta-recommendations
meta_llm_kwargs	{}	dict	Kwargs for meta-recommendation LLMs
meta_max_recommendations	5	int	Max number of meta-recommendations
embedding_model	None	Optional[str]	Model for code embeddings
init_program_path	"initial.py"	Optional[str]	Path to initial program to evolve
results_dir	None	Optional[str]	Directory to save results (auto-generated if None)
max_novelty_attempts	3	int	Max attempts for novelty generation
code_embed_sim_threshold	1.0	float	Similarity threshold for code embeddings
novelty_llm_models	None	Optional[List[str]]	LLM models for novelty judgment
novelty_llm_kwargs	{}	dict	Kwargs for novelty LLMs
use_text_feedback	False	bool	Whether to use text feedback in evolution

DatabaseConfig Parameters (click to expand)

Key	Default Value	Type	Explanation
db_path	None	Optional[str]	Database file path (auto-generated if None)
num_islands	4	int	Number of evolution islands for diversity
archive_size	100	int	Size of program archive per island
elite_selection_ratio	0.3	float	Proportion of elite programs for inspiration
num_archive_inspirations	5	int	Number of archive programs to use as inspiration
num_top_k_inspirations	2	int	Number of top-k programs for inspiration
migration_interval	10	int	Generations between island migrations
migration_rate	0.1	float	Proportion of island population to migrate
island_elitism	True	bool	Keep best programs on their original islands
enforce_island_separation	True	bool	Enforce full separation between islands
parent_selection_strategy	"power_law"	str	Parent selection: "weighted", "power_law", "beam_search"
exploitation_alpha	1.0	float	Power-law exponent (0=uniform, 1=power-law)
exploitation_ratio	0.2	float	Chance to pick parent from archive
parent_selection_lambda	10.0	float	Sharpness of sigmoid for weighted selection
num_beams	5	int	Number of beams for beam search selection

JobConfig Parameters (click to expand)

LocalJobConfig (for local execution):

Key	Default Value	Type	Explanation
eval_program_path	"evaluate.py"	Optional[str]	Path to evaluation script
extra_cmd_args	{}	Dict[str, Any]	Additional command line arguments
time	None	Optional[str]	Time limit for job execution
conda_env	None	Optional[str]	Conda environment to run jobs in

SlurmDockerJobConfig (for SLURM with Docker):

Key	Default Value	Type	Explanation
eval_program_path	"evaluate.py"	Optional[str]	Path to evaluation script
extra_cmd_args	{}	Dict[str, Any]	Additional command line arguments
image	"ubuntu:latest"	str	Docker image to use
image_tar_path	None	Optional[str]	Path to Docker image tar file
docker_flags	""	str	Additional Docker flags
partition	"gpu"	str	SLURM partition to use
time	"01:00:00"	str	Job time limit
cpus	1	int	Number of CPUs to request
gpus	1	int	Number of GPUs to request
mem	"8G"	Optional[str]	Memory to request

SlurmCondaJobConfig (for SLURM with Conda):

Key	Default Value	Type	Explanation
eval_program_path	"evaluate.py"	Optional[str]	Path to evaluation script
extra_cmd_args	{}	Dict[str, Any]	Additional command line arguments
conda_env	""	str	Conda environment name
modules	[]	Optional[List[str]]	Environment modules to load
partition	"gpu"	str	SLURM partition to use
time	"01:00:00"	str	Job time limit
cpus	1	int	Number of CPUs to request
gpus	1	int	Number of GPUs to request
mem	"8G"	Optional[str]	Memory to request

Evaluation Setup & Initial Solution 🏃

To use EvolutionRunner, you need two key files: The evaluate.py script defines how to test and score your programs - it runs multiple evaluations, validates results, and aggregates them into metrics that guide the genesis evolution loop. The initial.py file contains your starting solution with the core algorithm that will be iteratively improved by LLMs across generations.

evaluate.py - Evaluation Script from genesis.core import run_genesis_eval def main(program_path: str, results_dir: str): metrics, correct, err = run_genesis_eval( program_path=program_path, results_dir=results_dir, experiment_fn_name="run_experiment", num_runs=3, # Multi-evals to aggreg. get_experiment_kwargs=get_kwargs, aggregate_metrics_fn=aggregate_fn, validate_fn=validate_fn, # Optional ) def get_kwargs(run_idx: int) -> dict: return {"param1": "value", "param2": 42} def aggregate_fn(results: list) -> dict: score = results[0] text = results[1] return { "combined_score": float(score), "public": {...}, # genesis-visible "private": {...}, # genesis-invisible "extra_data": {...}, # store as pkl "text_feedback": text, # str fb } if __name__ == "__main__": # argparse program path & dir main(program_path, results_dir)

initial.py - Starting Solution # EVOLVE-BLOCK-START def advanced_algo(): # This will be evolved return solution # EVOLVE-BLOCK-END def run_experiment(**kwargs): """Main called by evaluator""" result = solve_problem(kwargs) return result def solve_problem(params): solution = advanced_algo() return solution Key Points: Eval name matches experiment_fn_name Use EVOLVE-BLOCK-START and EVOLVE-BLOCK-END to mark evolution sections Return format matches validation expectations Dependencies must be available in env Results can be unpacked for metrics Auto-stores several results in results_dir Can add text feedback in genesis loop Higher combined_score values indicate better performance (maximization)

genesis Launcher with Hydra 🚀

genesis Launcher utilizes Hydra to configure and launch evolutionary experiments effortlessly. It supports concise configuration via Hydra's powerful override syntax, making it easy to manage and iterate scientific explorations.

# Run with pre-configured variant
genesis_launch variant=circle_packing_example

# Run with custom parameters
genesis_launch \
    task=circle_packing \
    database=island_large \
    evolution=small_budget \
    cluster=local \
    evo_config.num_generations=20

For comprehensive configuration options and advanced usage, see the Configuration Guide.

Interactive WebUI 🎨

Monitor your evolution experiments in real-time with Genesis's interactive web interface! The WebUI provides live visualization of the evolutionary process, genealogy trees, and performance metrics.

The Programs view showing evolution results from HNSW optimization, with sortable columns for generation, score, cost, and complexity metrics.

Quick Start

Launch the WebUI alongside your evolution experiment:

# Start your evolution experiment
genesis_launch variant=circle_packing_example

# In another terminal, start the frontend
cd genesis/webui/frontend
npm install
npm run dev

For detailed WebUI documentation, see the WebUI Guide.

Related Open-Source Projects 🧑‍🔧

• Shinka AI: The original implementation that Genesis is based on - a platform for LLM-driven program evolution
• OpenEvolve: An open-source implementation of AlphaEvolve
• LLM4AD: A Platform for Algorithm Design with Large Language Model
• Scale AgentEx: Automated experimentation and optimization for AI agents

Acknowledgments 🙏

Genesis is built upon the excellent work of the Shinka AI project. We extend our gratitude to the original authors and contributors for creating such a robust foundation for LLM-driven code evolution.

Citation ✍️

If you use Genesis in your research, please cite:

@misc{genesis2025,
  title={Genesis: Platform Experiments for LLM-Driven Program Evolution},
  author={Pearse, George},
  howpublished={\url{https://genesis.ai}},
  year={2025}
}

And please also consider citing the original Shinka AI work that this is based on.