1 code implementation • 24 Feb 2024 • Danial Yazdani, Juergen Branke, Mohammad Sadegh Khorshidi, Mohammad Nabi Omidvar, XiaoDong Li, Amir H. Gandomi, Xin Yao
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.
1 code implementation • 24 Aug 2023 • Mai Peng, Zeneng She, Delaram Yazdani, Danial Yazdani, Wenjian Luo, Changhe Li, Juergen Branke, Trung Thanh Nguyen, Amir H. Gandomi, Yaochu Jin, Xin Yao
In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB).
1 code implementation • 8 Apr 2023 • George Watkins, Giovanni Montana, Juergen Branke
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.
no code implementations • 2 Feb 2023 • Jack M. Buckingham, Sebastian Rojas Gonzalez, Juergen Branke
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.
no code implementations • 30 Sep 2022 • Juan Ungredda, Michael Pearce, Juergen Branke
Bayesian optimization is a powerful collection of methods for optimizing stochastic expensive black box functions.
no code implementations • 5 Sep 2022 • Sebastian Rojas Gonzalez, Juergen Branke, Inneke Van Nieuwenhuyse
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).
1 code implementation • 30 Jul 2022 • Lennart Schneider, Florian Pfisterer, Paul Kent, Juergen Branke, Bernd Bischl, Janek Thomas
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.
no code implementations • 15 Jun 2022 • Florian Karl, Tobias Pielok, Julia Moosbauer, Florian Pfisterer, Stefan Coors, Martin Binder, Lennart Schneider, Janek Thomas, Jakob Richter, Michel Lang, Eduardo C. Garrido-Merchán, Juergen Branke, Bernd Bischl
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
1 code implementation • 23 Jul 2021 • Mohammad Nabi Omidvar, Danial Yazdani, Juergen Branke, XiaoDong Li, Shengxiang Yang, Xin Yao
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.
1 code implementation • 11 Jun 2021 • Danial Yazdani, Michalis Mavrovouniotis, Changhe Li, Wenjian Luo, Mohammad Nabi Omidvar, Amir H. Gandomi, Trung Thanh Nguyen, Juergen Branke, XiaoDong Li, Shengxiang Yang, Xin Yao
This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization.
no code implementations • 27 May 2021 • Juan Ungredda, Juergen Branke
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.
no code implementations • 27 May 2021 • Juan Ungredda, Mariapia Marchi, Teresa Montrone, Juergen Branke
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.
no code implementations • 5 Feb 2021 • Manuel López-Ibáñez, Juergen Branke, Luís Paquete
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.
no code implementations • 31 Dec 2020 • Matthew Groves, Juergen Branke
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.
no code implementations • 31 May 2020 • Juan Ungredda, Michael Pearce, Juergen Branke
Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution.
no code implementations • 8 May 2020 • Paul Kent, Juergen Branke
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.
no code implementations • 20 Nov 2019 • Jordan MacLachlan, Yi Mei, Juergen Branke, Mengjie Zhang
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.
no code implementations • 21 Oct 2019 • Michael Pearce, Matthias Poloczek, Juergen Branke
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning.