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Title:Opt2Vec - a continuous optimization problem representation based on the algorithm's behavior : A case study on problem classification
Authors:ID Korošec, Peter, Institut "Jožef Stefan" (Author)
ID Eftimov, Tome, Institut "Jožef Stefan" (Author)
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Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo IJS - Jožef Stefan Institute
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.
Publication status:Published
Publication version:Version of Record
Submitted for review:27.06.2024
Article acceptance date:28.06.2024
Publication date:03.07.2024
Publisher:Elsevier
Year of publishing:2024
Number of pages:20 str.
Numbering:Vol. 680, Art. 121134
Source:Nizozemska
PID:20.500.12556/DiRROS-20970 New window
UDC:004
ISSN on article:0020-0255
DOI:10.1016/j.ins.2024.121134 New window
COBISS.SI-ID:207123459 New window
Copyright:© 2024 The Author(s)
Publication date in DiRROS:11.12.2024
Views:214
Downloads:61
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KOROŠEC, Peter and EFTIMOV, Tome, 2024, Opt2Vec - a continuous optimization problem representation based on the algorithm’s behavior : A case study on problem classification. Information sciences [online]. 2024. Vol. 680, no. 121134. [Accessed 7 April 2025]. DOI 10.1016/j.ins.2024.121134. Retrieved from: https://dirros.openscience.si/IzpisGradiva.php?lang=eng&id=20970
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Record is a part of a journal

Title:Information sciences
Shortened title:Inf. sci.
Publisher:North-Holland
ISSN:0020-0255
COBISS.SI-ID:25613056 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0098
Name:Računalniške strukture in sistemi
Funder:ARIS - Slovenian Research and Innovation Agency
Project number:J2-4460
Name:Auto-OPT: Avtomatizirana izbira in konfiguracija eno-kriterijskih zveznih optimizacijskih algoritmov
Funder:ARIS - Slovenian Research and Innovation Agency
Project number:N2-0239
Name:Učenje predstavitev pokrajin za razlago kakovosti stohastičnih optimizacijskih algoritmov

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:03.07.2024
Applies to:VoR

Secondary language

Language:Slovenian
Keywords:računalništvo, algoritmi, optimizacija


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