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Found 6 papers, 6 papers with code
Random Pareto front surfaces
As a motivating example, we investigate how these statistics can be used within a design of experiments setting, where the goal is to both infer and use the Pareto front surface distribution in order to make effective decisions.
Multi-objective optimisation via the R2 utilities
As part of our work, we show that these utilities are monotone and submodular set functions which can be optimised effectively using greedy optimisation algorithms.
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data.
Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles
Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e. g. economic gain vs. environmental impact.
ENTMOOT: A Framework for Optimization over Ensemble Tree Models
Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications.
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded
Decision trees usefully represent sparse, high dimensional and noisy data.
