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Found 7 papers, 4 papers with code
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization
We discuss and illustrate why training processes to approximate Pareto fronts need to optimize on fronts of individual training samples instead of on only the front of average losses.
Identifying Properties of Real-World Optimisation Problems through a Questionnaire
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences.
Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent
On several bi-objective benchmarks, we find that gradient-based algorithms outperform the tested EAs by obtaining a better hypervolume with fewer evaluations whenever exact gradients of the multiple objective functions are available and in case of small evaluation budgets.
Optimization and Control
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems
This work aims to identify properties of real-world problems through a questionnaire on real-world single-, multi-, and many-objective optimization problems.
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
We tested the approach on 22, 206 pairs of 2D slices with varying levels of intensity, affine, and elastic transformations.
A Drug Recommendation System (Dr.S) for cancer cell lines
Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information.
Simulation assisted machine learning
When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs.
