Benchmark set reduction for cheap empirical algorithmic studies

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dc.authoridMustafa Mısır / 0000-0002-6885-6775en_US
dc.authorscopusidMustafa Mısır / 36458858100
dc.authorwosidMustafa Mısır / A-6739-2010
dc.contributor.authorMısır, Mustafa
dc.date.accessioned2021-12-03T07:14:40Z
dc.date.available2021-12-03T07:14:40Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe present paper introduces a benchmark set reduction strategy that can degrade the experimental evaluation cost for the algorithmic studies. Algorithm design is an iterative development process within in a test-revise loop. Starting a devised algorithm's initial version, it needs to be tested for revealing its advantages and drawbacks. Its shortcomings lead the designers to modify the algorithm. Then, the modified algorithm is again tested. This two-step pipeline is repeated until the algorithm meets the expectations such as delivering the state-of-the-art results on a set of benchmarks for a specific problem. That recurring testing step can be a burden for whom with limited computational resources. The mentioned computational cost can mainly occur due either high per instance runtime cost or having a large benchmark set. This study focuses on the cases when a target algorithm needs to be assessed across large benchmark sets. The idea is to automatically extract problem instance representation through an instance-algorithm performance data. The derived representation in the form of latent features is utilized to determine a small yet a representative subset of a given large instance set. The proposed strategy is investigated on the Traveling Thief Problem of 9720 instances. The corresponding performance data is collected by the help of 21 TTP algorithms. The resulting computational analysis showed that the proposed method is capable of substantially minimizing the benchmark instance set size.en_US
dc.identifier.citationMısır, M. (2021, June). Benchmark Set Reduction for Cheap Empirical Algorithmic Studies. In 2021 IEEE Congress on Evolutionary Computation (CEC) (pp. 871-877). IEEE.en_US
dc.identifier.doi10.1109/CEC45853.2021.9505012en_US
dc.identifier.endpage877en_US
dc.identifier.isbn9781728183923
dc.identifier.scopus2-s2.0-85124604588en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage871en_US
dc.identifier.urihttps://doi.org/10.1109/CEC45853.2021.9505012
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2298
dc.identifier.wosWOS:000703866100110en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMısır, Mustafa
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021)en_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBenchmark Set Reductionen_US
dc.subjectEmpirical Analysisen_US
dc.subjectTraveling Thief Problemen_US
dc.titleBenchmark set reduction for cheap empirical algorithmic studiesen_US
dc.typeOtheren_US

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