Dataset:
Landscape-based Evolutionary Algorithms for Dynamic Optimization Problems
Landscape-based Evolutionary Algorithms for Dynamic Optimization Problems
dc.contributor.other | Sarker, Ruhul | |
dc.contributor.other | Elsayed, Saber | |
dc.contributor.other | Essam, Daryl | |
dc.contributor.other | Wright, Fiona | |
dc.date.accessioned | 2022-08-05T05:48:26Z | |
dc.date.available | 2022-08-05T05:48:26Z | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-08-04T11:46:18Z | |
dc.description.abstract | These data include c++ code, experimental data and analysis results of thesis “Landscape-based Evolutionary Algorithms for Dynamic Optimization Problems” by Kangjing Li. | |
dc.identifier.uri | http://hdl.handle.net/1959.4/100526 | |
dc.language | English | |
dc.language.iso | en | |
dc.rights | GPL | |
dc.rights.uri | https://www.gnu.org/licenses/gpl-3.0.en.html | |
dc.subject.other | Evolutionary Computation | |
dc.subject.other | Dynamic Optimization | |
dc.subject.other | Landscape Similarity Check | |
dc.title | Landscape-based Evolutionary Algorithms for Dynamic Optimization Problems | |
dc.type | Dataset | |
dcterms.accessRights | open access | |
dcterms.rightsHolder | UNSW | |
dspace.entity.type | Dataset | |
unsw.accessRights.uri | https://purl.org/coar/access_right/c_abf2 | |
unsw.contributor.leadChiefInvestigator | Sarker, Ruhul | |
unsw.contributor.researchDataCreator | Li, Kangjing | |
unsw.coverage.temporalFrom | 2017-09-25 | |
unsw.coverage.temporalTo | 2022-03-11 | |
unsw.date.embargo | 2023-02-12 | |
unsw.date.workflow | 2022-08-05 | |
unsw.description.embargoNote | Embargoed until 2023-02-12 | |
unsw.identifier.doi | https://doi.org/10.26190/unsworks/24233 | |
unsw.isPublicationRelatedToDataset | https://doi.org/10.1109/ACCESS.2020.3026339 | |
unsw.isPublicationRelatedToDataset | https://doi.org/10.1109/CEC.2019.8790303 | |
unsw.relation.FunderRefNo | DP190102637 | |
unsw.relation.faculty | UNSW Canberra | |
unsw.relation.fundingAgency | AUSTRALIAN RESEARCH COUNCIL | |
unsw.relation.fundingScheme | DISCOVERY PROJECT | |
unsw.relation.projectDesc | the project aims to develop a novel framework for solving planning problems under changing environments with uncertainties. this research is driven by the fact, that there is a huge gap between current research and the methodology needed to solve practical planning problems. in the proposed framework, three algorithms will be developed and integrated to generate robust solutions for planning under dynamic changes with uncertainties. the intended scientific outcomes include a novel framework with new techniques, developed by exploiting the assumptions of existing methodologies. practical outcomes will include: a robust planning tool, strong research training, and high impact publications. | |
unsw.relation.projectEndDate | 2023-12-31 | |
unsw.relation.projectStartDate | 2019-04-01 | |
unsw.relation.projectTitle | robust evolutionary analytics for changing and uncertain environments | |
unsw.relation.school | School of Engineering and Information Technology | |
unsw.relation.school | School of Engineering and Information Technology | |
unsw.relation.school | School of Engineering and Information Technology | |
unsw.relation.school | School of Professional Studies | |
unsw.relation.school | School of Engineering and Information Technology | |
unsw.relation.unswGrantNo | RG189092 | |
unsw.subject.fieldofresearchcode | 460204 Fuzzy computation | |
unsw.subject.fieldofresearchcode | 461305 Data structures and algorithms | |
unsw.subject.fieldofresearchcode | 490108 Operations research |