Surrogate modeling and optimization for scientific machine learning (SciML)

Surrogate Optimization Toolbox for Python

NOMAD - A blackbox optimization software

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

Heuristic global optimization algorithms in Python

This repository contains the packages that build the problem objects for the desdeo framework.

Python library for parallel multiobjective simulation optimization

This is the official repository of the AI for TSP competition at IJCAI 2021

Surrogate model library for Derivative-Free Optimization

Python platform for parallel Surrogate-Based Optimization

Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions

MVRSM algorithm for optimising mixed-variable expensive cost functions.

Multiobjective Adaptive Surrogate Modeling-based Optimization Toolbox I

🌿 Introduce surrogate based-optimization beside evolutionary algorithms that can significantly influence on the effort being spent for multi-objective parameter tuning

DEFT-FUNNEL: An open-source global optimization solver for constrained grey-box and black-box problems in Matlab.

Revised MO-ASMO Algorithm

Implementation of the perceptron algorithm on MATLAB for classification

surF - a surrogate modeling method based on Discrete Fourier Transform

Python package for design of experiments

SKSurrogate is a suite of tools that implements surrogate optimization for expensive functions based on scikit-learn. The main purpose of SKSurrogate is to facilitate hyperparameter optimization for machine learning models and optimized pipeline design (AutoML).