AutoOED
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About
Introduction
Background
Platform Features
Platform Design
Supported Algorithms
Getting Started
Installation
Basic Usage
Example: Simulation Experiment
Example: Physical Experiment
User Manual
Overview
Software Entry
Building Problem
Evaluation Program
Building Experiment
Running Optimization
Statistics
Database
Exporting Data
API Reference
MOBO Algorithms
Surrogate Models
Acquisition Functions
Multi-Objective Solvers
Selection Criteria
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Welcome to AutoOED’s documentation!
¶
About
Introduction
Applications
Requirements
Background
Optimal Experimental Design
Multi-Objective Optimization
Multi-Objective Bayesian Optimization
References
Platform Features
Intuitive GUI
Modular Structure
Automation of Experimental Design
Sequential and batch evaluations
Data-Efficient Experimentation
Platform Design
Supported Algorithms
Multi-Objective Bayesian Optimization
References
Getting Started
Installation
Executable File
Source Code
Extra Steps for Custom Evaluation Programs
Basic Usage
Step 1: Starting Software
Step 2: Building Problem
Step 3: Building Experiment
Step 4: Running Optimization
Concluding Remarks
Reference
Example: Simulation Experiment
Problem Setup
Prerequisites
Step 1: Building Problem
Step 2: Building Experiment
Step 3: Running Optimization
Code of Evaluation Program
Example: Physical Experiment
Problem Setup
Prerequisites
Step 1: Building Problem
Step 2: Building Experiment
Step 3: Running Optimization
User Manual
Overview
Software Entry
Starting the Software
Managing Experiments
Main Interface
Building Problem
Design Space
Performance Space
Constraints
Predefined Test Problems
Evaluation Program
Performance Evaluation
Constraint Evaluation
Building Experiment
Creating Configuration through GUI
Loading from Configuration File
Running Optimization
Manual Mode
Auto Mode
Interrupt
Statistics
Hypervolume
Model Prediction Error
References
Database
Columns
Entering Design
Entering Performance
Display Settings
Exporting Data
Database
Statistics
Figures
API Reference
MOBO Algorithms
Base Class
DGEMO
TSEMO
USeMO-EI
MOEA/D-EGO
ParEGO
Custom Algorithm Class
Surrogate Models
Base Class
Gaussian Process
Neural Network
Bayesian Neural Network
Acquisition Functions
Base Class
Expected Improvement
Identity Function
Probability of Improvement
Thompson Sampling
Upper Confidence Bound
Multi-Objective Solvers
Base Class
NSGA-II
MOEA/D
ParEGO
ParetoFrontDiscovery
Selection Criteria
Base Class
Direct Selection
Hypervolume Improvement
Random Selection
Uncertainty
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