A case study on problem classification
Peter Korošec (Author), Tome Eftimov (Author)

Abstract

Characterization of the optimization problem is a crucial task in many recent optimization research topics (e.g., explainable algorithm performance assessment, and automated algorithm selection and configuration). The state-of-the-art approaches use exploratory landscape analysis to represent the optimization problem, where for each one, a set of features is extracted using a set of candidate solutions sampled by a sampling strategy over the whole decision space. This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an optimization problem. The new problem representation is learned using the information from the states/positions in the optimization run trajectory (i.e., the candidate solutions visited by the algorithm). With the novel representation, the problem can be characterized dynamically during the optimization run, instead of using a set of candidate solutions from the whole decision space that have never been observed by the algorithm. The novel optimization problem representation is called Opt2Vec and uses an autoencoder type of neural network to encode the information found in the interaction between an optimization algorithm and optimization problem into an embedded subspace. The Opt2Vec representation efficiency is shown by enabling different optimization problems to be successfully identified using only the information obtained from the optimization run trajectory.

Keywords

računalništvo;algoritmi;optimizacija;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: IJS - Jožef Stefan Institute
Publisher: Elsevier
UDC: 004
COBISS: 207123459 Link will open in a new window
ISSN: 0020-0255
Views: 28
Downloads: 7
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Other data

Secondary language: Slovenian
Secondary keywords: računalništvo;algoritmi;optimizacija;
Pages: 20 str.
Volume: ǂVol. ǂ680
Issue: ǂArt. ǂ121134
Chronology: 2024
DOI: 10.1016/j.ins.2024.121134
ID: 25491646