surrogate-based-optimization

Surrogate modeling and optimization for scientific machine learning (SciML)

Surrogate Optimization Toolbox for Python

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

NOMAD - A blackbox optimization software

Heuristic global optimization algorithms in Python

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

Surrogate model library for Derivative-Free Optimization

Multiobjective Adaptive Surrogate Modeling-based Optimization Toolbox I

A short course on simulation-based infernce for physics at YSDA in April 2021

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

Revised MO-ASMO Algorithm

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

Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.

surF - a surrogate modeling method based on Discrete Fourier Transform

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).

Implementation of the perceptron algorithm on MATLAB for classification

Online selection hyper-heuristic with generic parameter control in low-level heuristics (meta-heuristic).

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

MVRSM algorithm for optimising mixed-variable expensive cost functions.

Implementation of the PAMELI algorithm for computationally expensive multi-objective optimization

Code written for the BSc Project: Estimating Control Landscapes with Neural Networks by Susan Chen and Katie Xiao as part of our Imperial College London Physics degrees.

Python package for design of experiments