A Black-Box Discrete Optimization Benchmarking (BB-DOB) Pipeline Survey: Taxonomy, Evaluation, and Ranking

Aleš Zamuda, Miguel Nicolau, Christine Zarges

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

7 Citations (SciVal)
520 Downloads (Pure)

Abstract

This paper provides a taxonomical identification survey of classes in discrete optimization challenges that can be found in the literature including a proposed pipeline for benchmarking, inspired by previous computational optimization competitions. Thereby, a Black-Box Discrete Optimization Benchmarking (BB-DOB) perspective is presented for the BB-DOB@GECCO Workshop. It is motivated why certain classes together with their properties (like deception and separability or toy problem label) should be included in the perspective. Moreover, guidelines on how to select significant instances within these classes, the design of experiments setup, performance measures, and presentation methods and formats are discussed.
Original languageEnglish
Title of host publicationGECCO '18
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery
Pages1777-1782
Publication statusPublished - 06 Jul 2018
EventGECCO 2018: The Genetic and Evolutionary Computation Conference - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018
http://gecco-2018.sigevo.org

Conference

ConferenceGECCO 2018: The Genetic and Evolutionary Computation Conference
Country/TerritoryJapan
CityKyoto
Period15 Jul 201819 Jul 2018
Internet address

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