Automatic surrogate modelling technique selection based on features of optimization problems
Saini, B. S., Lopez-Ibanez, M., & Miettinen, K. (2019). Automatic surrogate modelling technique selection based on features of optimization problems. In GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume (pp. 1765-1772). ACM. https://doi.org/10.1145/3319619.3326890
© 2019 Association for Computing Machinery
A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the literature, there is no standard procedure that will select the best technique for a given problem. In this work, we propose the automatic selection of a surrogate modelling technique based on exploratory landscape features of the optimization problem that underlies the given dataset. The overall idea is to learn offline from a large pool of benchmark problems, on which we can evaluate a large number of surrogate modelling techniques. When given a new dataset, features are used to select the most appropriate surrogate modelling technique. The preliminary experiments reported here suggest that the proposed automatic selector is able to identify high-accuracy surrogate models as long as an appropriate classifier is used for selection. ...
Parent publication ISBN978-1-4503-6748-6
ConferenceGenetic and Evolutionary Computation Conference
Is part of publicationGECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis research was partly supported by the Academy of Finland (grant number 287496, project DESDEO). This related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyvaskyla.
Showing items with similar title or keywords.
Rasku, Jussi; Musliu, Nysret; Kärkkäinen, Tommi (Springer, 2019)Many of the algorithms for solving vehicle routing problems expose parameters that strongly influence the quality of obtained solutions and the performance of the algorithm. Finding good values for these parameters is a ...
Feature-Based Benchmarking of Distance-Based Multi/Many-objective Optimisation Problems : A Machine Learning Perspective Liefooghe, Arnaud; Verel, Sébastien; Chugh, Tinkle; Fieldsend, Jonathan; Allmendinger, Richard; Miettinen, Kaisa (Springer, 2023)We consider the application of machine learning techniques to gain insights into the effect of problem features on algorithm performance, and to automate the task of algorithm selection for distance-based multi- and ...
Jin, Yaochu; Wang, Handing; Chugh, Tinkle; Guo, Dan; Miettinen, Kaisa (Institute of Electrical and Electronics Engineers, 2019)Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may ...
A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization Chugh, Tinkle; Jin, Yaochu; Miettinen, Kaisa; Hakanen, Jussi; Sindhya, Karthik (Institute of Electrical and Electronics Engineers, 2018)We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed ...
Chugh, Tinkle (University of Jyväskylä, 2017)Multiobjective optimization problems (MOPs) with a large number of conﬂicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations ...