| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34R/43F85D2 | |||
| Repository | sid.inpe.br/mtc-m21c/2020/10.23.12.00 (restricted access) | |||
| Last Update | 2020:10.23.12.00.29 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21c/2020/10.23.12.00.29 | |||
| Metadata Last Update | 2022:01.04.01.35.29 (UTC) administrator | |||
| DOI | 10.1016/j.asoc.2020.106760 | |||
| ISSN | 1568-4946 1872-9681 | |||
| Citation Key | SantiagoJúniorÖzcaCarv:2020:HyBaRe | |||
| Title | Hyper-Heuristics based on reinforcement learning, balanced heuristic selection and group decision acceptance  | |||
| Year | 2020 | |||
| Month | Dec. | |||
| Access Date | 2024, Apr. 16 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 2234 KiB | |||
| 2. Context | ||||
| Author | 1 Santiago Júnior, Valdivino Alexandre de 2 Özcan, Ender 3 Carvalho, Vinicius Renan de | |||
| Resume Identifier | 1 8JMKD3MGP5W/3C9JJB5 | |||
| Group | 1 LABAC-COCTE-INPE-MCTIC-GOV-BR | |||
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 University of Nottingham 3 Universidade de São Paulo (USP) | |||
| Author e-Mail Address | 1 valdivino.santiago@inpe.br 2 Ender.Ozcan@nottingham.ac.uk 3 vrcarvalho@usp.br | |||
| Journal | Applied Soft Computing Journal | |||
| Volume | 97 | |||
| Pages | e106760 | |||
| Secondary Mark | A2_INTERDISCIPLINAR A2_ENGENHARIAS_IV A2_ENGENHARIAS_III A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_ENGENHARIAS_II B1_BIOTECNOLOGIA | |||
| History (UTC) | 2020-10-23 12:00:29 :: simone -> administrator :: 2020-10-23 12:00:30 :: administrator -> simone :: 2020 2020-10-23 12:00:51 :: simone -> administrator :: 2020 2020-10-24 10:46:16 :: administrator -> simone :: 2020 2020-12-14 11:54:54 :: simone -> administrator :: 2020 2022-01-04 01:35:29 :: administrator -> simone :: 2020 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Keywords | Hyper-heuristic Reinforcement learning Balanced heuristic selection Group decision-making Multi-objective evolutionary algorithms Multi-objective optimisation | |||
| Abstract | In this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement LearnIng, Balanced Heuristic Selection and Group Decision AccEptance (HRISE), controlling a set of Multi-Objective Evolutionary Algorithms (MOEAs) as Low-Level (meta)Heuristics (LLHs). Along with the use of multiple MOEAs, we believe that having a robust LLH selection method as well as several move acceptance methods at our disposal would lead to an improved general-purpose method producing most adequate solutions to the problem instances across multiple domains. We present two learning hyper-heuristics based on the HRISE framework for multi-objective optimisation, each embedding a group decision-making acceptance method under a different rule: majority rule (HRISE_M) and responsibility rule (HRISE_R). A third hyper-heuristic is also defined where both a random LLH selection and a random move acceptance strategy are used. We also propose two variants of the late acceptance method and a new quality indicator supporting the initialisation of selection hyper-heuristics using low computational budget. An extensive set of experiments were performed using 39 multi-objective problem instances from various domains where 24 are from four different benchmark function classes, and the remaining 15 instances are from four different real-world problems. The cross-domain search performance of the proposed learning hyperheuristics indeed turned out to be the best, particularly HRISE_R, when compared to three other selection hyper-heuristics, including a recently proposed one, and all low-level MOEAs each run in isolation. | |||
| Area | COMP | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Hyper-Heuristics based on... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | valdivino_hyper.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | denypublisher denyfinaldraft24 | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/3ESGTTP | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/09.22.23.14 2 sid.inpe.br/mtc-m21/2012/07.13.15.01.24 1 | |||
| Dissemination | WEBSCI; PORTALCAPES; COMPENDEX. | |||
| Host Collection | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||