paper-with-me

홈 › Papers

Identifying Properties of Real-World Optimisation Problems through a Questionnaire

2020-11-11 · Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tušar

Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences. However, it is not clear how closely benchmarks match the properties of real-world problems because these properties are largely unknown. This work investigates the properties of real-world problems through a questionnaire to enable the design of future benchmark problems that more closely resemble those found in the real world. The results, while not representative as they are based on only 45 responses, indicate that many problems possess at least one of the following properties: they are constrained, deterministic, have only continuous variables, require substantial computation times for both the objectives and the constraints, or allow a limited number of evaluations. Properties like known optimal solutions and analytical gradients are rarely available, limiting the options in guiding the optimisation process. These are all important aspects to consider when designing realistic benchmark problems. At the same time, the design of realistic benchmarks is difficult, because objective functions are often reported to be black-box and many problem properties are unknown. To further improve the understanding of real-world problems, readers working on a real-world optimisation problem are encouraged to fill out the questionnaire: https://tinyurl.com/opt-survey

📄 PDF Abstract BibTeX arXiv:2011.05547

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Bayesian Optimisation Against Climate Change: Applications and Benchmarks

2023-06-07 · Sigrid Passano Hellan, Christopher G. Lucas, Nigel H. Goddard

Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can impr…

Bayesian Optimisation

The multi-objective optimisation of breakwaters using evolutionary approach

2020-04-06 · Nikolay O. Nikitin, Iana S. Polonskaia, Anna V. Kalyuzhnaya, Alexander V. Boukhanovsky

In engineering practice, it is often necessary to increase the effectiveness of existing protective constructions for ports and coasts (i. e. breakwaters) by extending their configuration, because existing configurations…

Two-Timescale Stochastic Approximation for Bilevel Optimisation Problems in Continuous-Time Models

2022-06-14 · Louis Sharrock

We analyse the asymptotic properties of a continuous-time, two-timescale stochastic approximation algorithm designed for stochastic bilevel optimisation problems in continuous-time models. We obtain the weak convergence …

Bayesian Optimisation for Constrained Problems

2021-05-27 · 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. A popular approach to tackle such p…

Bayesian Optimisation

Decision-Focused Forecasting: A Differentiable Multistage Optimisation Architecture

2024-05-23 · Egon Peršak, Miguel F. Anjos

Most decision-focused learning work has focused on single stage problems whereas many real-world decision problems are more appropriately modelled using multistage optimisation. In multistage problems contextual informat…

Decision MakingDecision Making Under Uncertainty