| dc.contributor.author | Foss, Fredrik | |
| dc.contributor.author | Mengshoel, Ole Jakob | |
| dc.date.accessioned | 2022-04-06T08:56:59Z | |
| dc.date.available | 2022-04-06T08:56:59Z | |
| dc.date.created | 2022-01-20T12:48:24Z | |
| dc.date.issued | 2021 | |
| dc.identifier.isbn | 978-1-4503-8351-6 | |
| dc.identifier.uri | https://hdl.handle.net/11250/2990115 | |
| dc.description.abstract | Multimodal functions play a central role in artificial intelligence. In this paper we attempt to address limitations in existing research on multimodal function optimization by developing a novel multi-method memetic algorithm (MMA). We empirically test MMA on synthetic and natural combinatorial optimization problems, including feature selection. Our initial experiments suggest that MMA preserves diversity well and consistently finds good solutions. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Association for Computing Machinery (ACM) | en_US |
| dc.relation.ispartof | GECCO '21: Genetic and Evolutionary Computation Conference, Companion Volume | |
| dc.title | A Multimethod Approach to Multimodal Function Optimization | en_US |
| dc.type | Chapter | en_US |
| dc.description.version | publishedVersion | en_US |
| dc.rights.holder | © 2021 Copyright held by the owner/author(s). | en_US |
| dc.source.pagenumber | 235-236 | en_US |
| dc.identifier.doi | 10.1145/3449726.3459435 | |
| dc.identifier.cristin | 1986156 | |
| cristin.ispublished | true | |
| cristin.fulltext | preprint | |
| cristin.qualitycode | 1 | |
