AileMM's Stars
NikoZHAI/samolib
Surrogate Assisted Multi-objective Opitimization LIBrary
evanchodora/rbf_surrogate
Python-based Radial Basis Function surrogate modeling tool
YuejiaoGong/CL-DDEA
Contrastive Learning Surrogate for Evolutionary Computation
GoojungMyeon/Mathematics-Surrogate-assisted-Genetic-algorithm
XunzhaoYu/SAB-DE
Surrogate-Assisted Bilevel Differential Evolution (SAB-DE)
jldaniel/Touchstone
Test functions for optimization and surrogate modeling.
GuillaumeBriffoteaux/pySBO
Python platform for parallel Surrogate-Based Optimization
gongw03/ASMO
This is a standalone version of ASMO, a surrogate based single objective optimization algorithm.
cog-imperial/GPdoemd
Design of experiments for model discrimination using Gaussian process surrogate models
AlgTUDelft/ExpensiveOptimBenchmark
Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
jajcayn/pygpso
Gaussian-Processes Surrogate Optimisation in python
alfiyandyhr/nn_ga_benchmark
NN+GA: A Surrogate-Based Optimization Framework Using Neural Network and Genetic Algorithm
dmosopt/dmosopt
Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
Yatoom/Hyperboost
Bayesian hyperparameter optimization with a gradient boosting surrogate model
WindAsMe/SEA-HHA
Surrogate ensemble assisted Hyper-heuristics algorithm for Expensive Optimization Problems
RenzhiChen/MSAEA
Multioutput Surrogate Assisted Evolutionary Algorithm for Expensive Multi-Modal Optimization
spyrostsat/AMSEEAS
Official repository of AMSEEAS surrogate-based global optimization algorithm
XunzhaoYu/Experience-Based-SAEA
Experience-Based Surrogate-Assisted Evolutionary Algorithm Framework for Expensive Optimization Problems.
drkupi/pySOT-MO
Extension to the PySOT library that allows parallel surrogate Multi-Objective optimization when used with POAP.
ShuleiLiu/SAGIF
Surrogate-Assisted Environmental Selection for Fast Hypervolume-based Many-Objective Optimization
ShuleiLiu/EAHVFA
A Surrogate-Assisted Evolutionary Algorithm with Hypervolume Triggered Fidelity Adjustment for Noisy Multiobjective Integer Programming
cornelius-braun/constrainedBO
Bayesian Optimization for black-box constrained problems with GP surrogate models based on trieste.
brain-research/guided-evolutionary-strategies
Guided Evolutionary Strategies
ahcheriet/CEC2018_DMOP
CEC2018 DF Dynamic Multiobjective optimization benchmarks
lordflavio/PEMF-Time-Series
Predictive Estimation of Model Fidelity (PEMF) is a model-independent approach to measure the fidelity of surrogate models or metamodels, such as Kriging, Radial Basis Functions (RBF), Support Vector Regression (SVR), and Neural Networks. It can be perceived as a novel sequential and predictive implementation of K-fold cross-validation. PEMF takes as input a model trainer (e.g., RBF-multiquadric or Kriging-Linear), sample data on which to train the model, and hyper-parameter values (e.g., shape factor in RBF) to apply to the model. As output, it provides a predicted estimate of the median and/or the maximum error in the surrogate model. PEMF has been reported to be more accurate and robust than typical leave-one-out cross-validation, in providing surrogate model error measures (for various benchmark functions). The current version of PEMF has been implemented with RBF (included in this package), Kriging (DACE package), and SVR (Libsvm package), PEMF (has been and) can be readily used for the following purposes: 1. Surrogate model validation 2. Surrogate model uncertainty analysis 3. Surrogate model selection 4. Surrogate-based optimization (to guide sequential sampling) Other perceived broader applications of PEMF include testing of machine learning models and uncertainty analysis with data-driven models (and other areas where leave-one-out or k-fold cross-validation is typically used).
gongw03/MO-ASMO
This is a standalone version of MO-ASMO, a surrogate based multi-objective optimization algorithm.
Technologicat/sudoku_lhs
Latin hypercube sampler with sudoku constraint
SMTorg/smt
Surrogate Modeling Toolbox
upb-lea/PSOAS
Particle Swarm Optimization Assisted by Surrogates
GuoxiaFu/SAEA-RFS
A Surrogate-Assisted Evolutionary Algorithm with Random Feature Selection for Large-Scale Expensive Problems