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Found 23 papers, 6 papers with code
Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble
Many real-world black-box optimization problems have multiple conflicting objectives.
Bayesian Optimization of Bilevel Problems
Bilevel optimization, a hierarchical mathematical framework where one optimization problem is nested within another, has emerged as a powerful tool for modeling complex decision-making processes in various fields such as economics, engineering, and machine learning.
Respecting the limit:Bayesian optimization with a bound on the optimal value
In many real-world optimization problems, we have prior information about what objective function values are achievable.
Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems
Bayesian optimization is a sample-efficient method for solving expensive, black-box optimization problems.
Identifying the Best Arm in the Presence of Global Environment Shifts
This paper formulates a new Best-Arm Identification problem in the non-stationary stochastic bandits setting, where the means of all arms are shifted in the same way due to a global influence of the environment.
Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems.
EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms
Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively.
Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks
The graph colouring problem consists of assigning labels, or colours, to the vertices of a graph such that no two adjacent vertices share the same colour.
Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations
Multi-objective Bayesian optimization aims to find the Pareto front of trade-offs between a set of expensive objectives while collecting as few samples as possible.
Efficient computation of the Knowledge Gradient for Bayesian Optimization
Bayesian optimization is a powerful collection of methods for optimizing stochastic expensive black box functions.
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).
Tackling Neural Architecture Search With Quality Diversity Optimization
Although considerable progress has been made in the field of multi-objective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve.
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.
Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
The Generalized Moving Peaks Benchmark (GMPB) is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics.
Bayesian Optimisation for Constrained Problems
Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions.
One Step Preference Elicitation in Multi-Objective Bayesian Optimization
We address this issue by using a multi-objective Bayesian optimization algorithm and allowing the DM to select a preferred solution from a predicted continuous Pareto front just once before the end of the algorithm rather than selecting a solution after the end.
Reproducibility in Evolutionary Computation
Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields.
Exploiting Transitivity for Top-k Selection with Score-Based Dueling Bandits
We extend this to selection problems where sampling results contain quantitative information by proposing a Thurstonian style model and adapting the Pairwise Optimal Computing Budget Allocation for subset selection (POCBAm) sampling method to exploit this model for efficient sample selection.
Bayesian Optimisation vs. Input Uncertainty Reduction
Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution.
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search
Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find a set of high-performing points from an objective function while enforcing behavioural diversity of the points over one or more interpretable, user chosen, feature functions.
Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems
Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem.
Bayesian Optimization Allowing for Common Random Numbers
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning.
