About

Learn more about the Surfaces project.

Project Overview

Surfaces is a Python library that provides single-objective black-box optimization test functions for benchmarking. It’s part of a larger ecosystem of optimization tools developed by Simon Blanke.

History

Surfaces was created to provide a standardized set of test functions for evaluating optimization algorithms, particularly those in the Gradient-Free-Optimizers and Hyperactive projects.

The library draws from the classic optimization literature, implementing well-known test functions like Ackley, Rosenbrock, and Rastrigin, while also introducing novel ML-based test functions that provide realistic hyperparameter optimization landscapes.

Related Projects

Surfaces is part of an ecosystem of optimization tools:

Gradient-Free-Optimizers

Gradient-Free-Optimizers provides implementations of gradient-free optimization algorithms, from simple hill climbing to Bayesian optimization.

Hyperactive

Hyperactive is a high-level hyperparameter optimization framework that uses Gradient-Free-Optimizers under the hood and provides integrations with scikit-learn, PyTorch, and more.

Author

Surfaces is developed and maintained by Simon Blanke.

• GitHub: @SimonBlanke

• Email: simon.blanke@yahoo.com

License

Surfaces is released under the MIT License, which allows for both personal and commercial use, modification, and distribution.

MIT License

Copyright (c) 2020 - |current_year| Simon Blanke

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

Acknowledgments

Thanks to all contributors and users of Surfaces who have helped improve the library through bug reports, feature requests, and code contributions.

The mathematical test functions are based on the extensive optimization literature. Key references include:

• Ackley, D. H. (1987). A Connectionist Machine for Genetic Hillclimbing.

• Rosenbrock, H. H. (1960). An Automatic Method for Finding the Greatest or Least Value of a Function.

• Rastrigin, L. A. (1974). Systems of Extremal Control.