weihong1021 Goto Github PK

2021-so-bco

2022-so-bo

Single Objective Bound Constrained Benchmark

aefs

Code for paper "Autoencoder Inspired Unsupervised Feature Selection"

awesome-kan

A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold Network field.

cnnoptimization

Using Particle Swarm Optimization (PSO) to Optimize a CNN (Convulsional Neural Network) - using an simple dataset (not using an image dataset)

code_islo_elm

A study on swarm intelligence optimizing neural networks for workload elasticity prediction

code_ocro_mlnn

Efficient Time-series Forecasting using Neural Network and Opposition-based Coral Reefs Optimization

code_otwo_elm

(Code) A new workload prediction model using extreme learning machine and enhanced tug of war optimization

codeforsaeasurvey

covid-19

covidpred

Machine Learning-based prediction of COVID-19 diagnosis based on symptoms

coyote-optimisation-algorithm-coa-matlab

Coyote Optimisation Algorithm - COA

cpsogsa-for-multilevel-image-thresholding

CPSOGSA is employed to find the optimal pixels in the benchmark images

dataset

Crop/Weed Field Image Dataset

ddea-se

Offline data-driven evolutionary optimization using selective surrogate ensembles

deeplearning

den-armoea

# Introduction of DNN-AR-MOEA This repository contains code necessary to reproduce the experiments presented in Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network. Gaussian processes are widely used in surrogate-assisted evolutionary optimization of expensive problems. We propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multi- and many-objective expensive optimization problems. mainlydue to the ability to provide a confidence level of their outputs,making it possible to adopt principled surrogate managementmethods such as the acquisition function used in Bayesian opti-mization. Unfortunately, Gaussian processes become less practi-cal for high-dimensional multi- and many-objective optimizationas their computational complexity is cubic in the number oftraining samples. # References If you found DNN-AR-MOEA useful, we would be grateful if you cite the following reference: Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network (IEEE Transactions on Systems, Man and Cybernetics: Systems).

diabetes-prediction-using-ml-and-dl

discovering-novel-prognostic-biomarkers-of-hepatocellular-carcinoma-using-xai

A multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for hepatocellular carcinoma prognosis.

dncnn

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)

dynamic-optimizing-strategy-of-ev-charging-using-pso

UCLA, Smart charging, minimize load variance, Particle Swarm Optimization

energy-management-hybrid-aircraft

Predictive energy management for hybrid-electric aircraft with parallel propulsion system.

energymanagementstrategy

A rule-based energy management strategies for hybrid vehicles using dynamic programming in Matlab

eode

Code for: Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner -- [IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB 24)]

fault-detection

CVA-PLSR for detecing faults in wind turbines

fault-detection-hvac

Python Source code and datasets used in my doctoral dissertation - Detection of faults in HVAC systems using tree-based ensemble models and dynamic thresholds

feature-selection

Find best features to be used with your dataset using forward selection, backward elimination, greedy backwards and forward and pruned forward selection.

featureselect

FeatureSelect

federated-learning

fednas

FedNAS: Federated Deep Learning via Neural Architecture Search