evopt: user friendly black-box numerical optimization

Optimization of the two parameter Ackley function

evopt is a Python package for efficient parameter optimization using the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm. It provides a straightforward way to find optimal parameters for complex problems, especially when the relationship between parameters and outcomes isn’t easily calculable.

Key Features

  • Simple interface - Optimize complex problems with minimal code

  • CMA-ES algorithm - Powerful, gradient-free optimization

  • Multi-objective optimization - Handle multiple targets with hard and soft constraints

  • Result visualization - Built-in plotting for optimization convergence and parameter relationships

  • Checkpointing - Resume interrupted optimizations from saved states

  • Parallel evaluation - Speed up optimization with multi-core processing

  • HPC support - Run on high-performance computing clusters with SLURM integration

Installation

pip install evopt

Quick Start

import evopt

# Define parameter space
params = {
    'x': (-5, 5),
    'y': (-5, 5),
}

# Define evaluator function
def evaluator(params):
    x, y = params['x'], params['y']
    return (1 - x)**2 + 100 * (y - x**2)**2  # Rosenbrock function

# Run optimization
results = evopt.optimise(
    params=params,
    evaluator=evaluator,
    batch_size=8
)

# Print best parameters found
print(f"Best parameters: {results.best_parameters}")
print(f"Final error: {results.final_error}")

Use Cases

  • Simulation Calibration: Find parameters that make simulations match reality

  • Engineering Design: Optimize dimensions, materials, and other design variables

  • Machine Learning: Tune hyperparameters for optimal model performance

  • Scientific Modeling: Estimate parameters in complex physical models

Documentation

User Guide:

Public API: