Python library for parallel multiobjective simulation optimization

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

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

Self-Supervised Deep Learning based Surrogate Models for Fault-Tolerant Edge Computing

MVRSM algorithm for optimising mixed-variable expensive cost functions.

Python platform for parallel Surrogate-Based Optimization

MITIM (MIT Integrated Modeling) Suite for Fusion Applications

Benchmark suite for active learning (with surrogate models) in scientific discovery, featuring standardised tasks in materials science and biology 🧪🤖

A python package for parameter uncertainty quantification and optimization

surF - a surrogate modeling method based on Discrete Fourier Transform

General-purpose library for fitting models to data with correlated Gaussian-distributed noise

Python package for design of experiments

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

A transformative approach to manufacturing optimization, focusing on the textile forming process. This research synergizes domain-specific knowledge with simulation modeling and introduces Bayesian optimization for efficient parameter space exploration.

This GOMORS algorithm is the modified version of what is uploaded in this repository: https://github.com/drkupi/GOMORS_pySOT.

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).

A Surrogate-Assisted Evolutionary Algorithm with Hypervolume Triggered Fidelity Adjustment for Noisy Multiobjective Integer Programming

Statistical learning models library for blackbox optimization

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

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