weihong1021 Goto Github PK
Type: User
Type: User
Single Objective Bound Constrained Benchmark
Code for paper "Autoencoder Inspired Unsupervised Feature Selection"
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.
Using Particle Swarm Optimization (PSO) to Optimize a CNN (Convulsional Neural Network) - using an simple dataset (not using an image dataset)
A study on swarm intelligence optimizing neural networks for workload elasticity prediction
Efficient Time-series Forecasting using Neural Network and Opposition-based Coral Reefs Optimization
(Code) A new workload prediction model using extreme learning machine and enhanced tug of war optimization
Machine Learning-based prediction of COVID-19 diagnosis based on symptoms
Coyote Optimisation Algorithm - COA
CPSOGSA is employed to find the optimal pixels in the benchmark images
Crop/Weed Field Image Dataset
Offline data-driven evolutionary optimization using selective surrogate ensembles
# 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).
A multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for hepatocellular carcinoma prognosis.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
UCLA, Smart charging, minimize load variance, Particle Swarm Optimization
Predictive energy management for hybrid-electric aircraft with parallel propulsion system.
A rule-based energy management strategies for hybrid vehicles using dynamic programming in Matlab
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)]
CVA-PLSR for detecing faults in wind turbines
Python Source code and datasets used in my doctoral dissertation - Detection of faults in HVAC systems using tree-based ensemble models and dynamic thresholds
Find best features to be used with your dataset using forward selection, backward elimination, greedy backwards and forward and pruned forward selection.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.