Search Results for author:
Found 6 papers, 0 papers with code
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO
Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives.
Bayesian Optimization of Multiple Objectives with Different Latencies
Multi-objective Bayesian optimization aims to find the Pareto front of optimal trade-offs between a set of expensive objectives while collecting as few samples as possible.
Bi-objective Ranking and Selection Using Stochastic Kriging
We consider bi-objective ranking and selection problems, where the goal is to correctly identify the Pareto optimal solutions among a finite set of candidates for which the two objective outcomes have been observed with uncertainty (e. g., after running a multiobjective stochastic simulation optimization procedure).
Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding
Adhesive joints are increasingly used in industry for a wide variety of applications because of their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance.
Multi-objective simulation optimization of the adhesive bonding process of materials
Automotive companies are increasingly looking for ways to make their products lighter, using novel materials and novel bonding processes to join these materials together.
A survey on multi-objective hyperparameter optimization algorithms for Machine Learning
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms.
