bazhiyong Goto Github PK
Name: Dr. Ba
Type: User
Company: xinjiang university
Bio: A doctoral student in industrial engineering from Xinjiang University
Location: Urmuqi china
Name: Dr. Ba
Type: User
Company: xinjiang university
Bio: A doctoral student in industrial engineering from Xinjiang University
Location: Urmuqi china
Huang, J., Chang, Q., & Arinez, J. (2020). Product Completion Time Prediction Using A Hybrid Approach Combining Deep Learning and System Model. Journal of Manufacturing Systems, 57, 311-322.
Bi-objective flexible job shop scheduling problems
Bayesian MPNNs for Molecular Property Prediction
Handling Imbalance Between Convergence and Diversity in the Decision Space in Evolutionary Multi-Modal Multi-Objective Optimization (IEEE Transactions on Evolutionary Computation)
An open source nesting application for laser cutters, plasma cutters and other CNC machines
# 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).
Hybrid differential evolution algorithm (HDE) to solve the Flexible Job Shop Scheduling Problem
this repository is used to reappear thesis《Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning》
Operations Research Application Project - Flow Shop Scheduling Based On Reinforcement Learning Algorithm
Learning how to implement GA and NSGA-II for job shop scheduling problem in python
Master's Thesis - Graph Neural Networks for Compact Representation for Job Shop Scheduling Problems: A Comparative Benchmark
use a new DNN net work to solute the job shop problem, deep learning method named HDNNM
this is test program
Multi-objective evolutionary algorithms integrated with different heuristic decoding methods for hybrid flow shop scheduling problem with worker constraint
This is a program to solve the job shop scheduling problem by using the parallel genetic algorithm
Job Shop Scheduling
Job Shop Scheduling metaheuristics
Job Shop Scheduling Problem Solver
The source code of LBD-MOEA, i.e., "A Multi-objective Evolutionary Algorithm for Finding Knee Regions Using Two Localized Dominance Relationships"
2D irregular bin packaging and nesting library written in modern C++
Analisys of experimental results MA vs. ILS-MOEA/D
MPI programming lessons in C and executable code examples
Nest2D is a 2D bin packaging tool for python.
Graphical algorithm to find possible solutions to 2D nesting problems.
A python library for the following Multiobjective Objectives Optimization Algorithms or Many Objectives Optimization Algorithms: C-NSGA II; CTAEA; GrEA; IBEA; MOEA/D; NAEMO; NSGA II; NSGA III; OMOPSO; PAES; RVEA; SMPSO; SPEA2; U-NSGA III
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
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.