Implementation of the PAMELI algorithm for computationally expensive multi-objective optimization
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Updated
Jun 30, 2020 - Python
Implementation of the PAMELI algorithm for computationally expensive multi-objective optimization
This repository contains the code for analysis on the computational aspects of robustness in surrogate-assisted robust optimization.
NKCS model for exploring aspects of (surrogate-assisted) coevolution.
Optimierungsroutine für rechenaufwendige Systeme
Source files of experiment resutls for the manusctipt that submitted to ESWA.
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.
Multi-objective optimization problem using the NSGA-2 and surrogate modelling to speed up the process.
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.
This repository contains code and data for optimizing punch and die design to minimize punched deviations in PCB registraion.
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.
General-purpose library for fitting models to data with correlated Gaussian-distributed noise
Python package for design of experiments
Demonstrating the use of Prefect to orchestrate the creation of machine learning surrogate models as applied to mechanistic crop models.
Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.
BOSS (Bayesian Optimization with Semiparametric Surrogate)
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