x-evolution 0.0.16

x-evolution

x-evolution

Implementation of various evolutionary algorithms, starting with evolutionary strategies

Install

$ pip install x-evolution

Usage

import torch
from x_evolution import EvoStrategy

# model

from torch import nn
model = torch.nn.Sequential(
    nn.Linear(8, 16),
    nn.ReLU(),
    nn.Linear(16, 4)
)

# evolution wrapper

evo_strat = EvoStrategy(
    model,
    environment = lambda model: torch.randint(0, 100, ()), # environment is just a function that takes in the individual model (with unique noise) and outputs the fitness - you can select for whatever you want here (rewards, model complexity, etc etc), does not have to be differentiable.
    noise_population_size = 30,
    num_generations = 100,
    learning_rate = 1e-3,
    noise_scale = 1e-3,
    params_to_optimize = None # defaults to all parameters, but can be [str {param name}] or [Parameter]
)

# do evolution with your desired fitness function for so many generations

evo_strat()

# model will be saved under checkpoints/ folder
# can also specify checkpoint_every at init and select the one with your favored fitness score for continued policy gradient learning etc

Distributed

Using the CLI from 🤗

$ accelerate config

Then

$ accelerate launch train.py

Citations

@article{Qiu2025EvolutionSA,
    title   = {Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning},
    author  = {Xin Qiu and Yulu Gan and Conor F. Hayes and Qiyao Liang and Elliot Meyerson and Babak Hodjat and Risto Miikkulainen},
    journal = {ArXiv},
    year    = {2025},
    volume  = {abs/2509.24372},
    url     = {https://api.semanticscholar.org/CorpusID:281674745}
}