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
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.
General-purpose library for fitting models to data with correlated Gaussian-distributed noise
This GOMORS algorithm is the modified version of what is uploaded in this repository: https://github.com/drkupi/GOMORS_pySOT.
Online selection hyper-heuristic with generic parameter control in low-level heuristics (meta-heuristic).
Optimierungsroutine für rechenaufwendige Systeme
Source files of experiment resutls for the manusctipt that submitted to ESWA.
This repository contains code and data for optimizing punch and die design to minimize punched deviations in PCB registraion.
Demonstrating the use of Prefect to orchestrate the creation of machine learning surrogate models as applied to mechanistic crop models.
A Surrogate-Assisted Evolutionary Algorithm with Hypervolume Triggered Fidelity Adjustment for Noisy Multiobjective Integer Programming
Statistical learning models library for blackbox optimization
Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.
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.
ANN based Surrogate model optimization of airfoil using XFOIL
This repository contains the code for analysis on the computational aspects of robustness in surrogate-assisted robust optimization.
A short course on simulation-based infernce for physics at YSDA in April 2021
A design optimization study of underwater vehicle using Bayesian optimization and deep learning based surrogate model
NKCS model for exploring aspects of (surrogate-assisted) coevolution.
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