Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/26781Registo completo
| Campo DC | Valor | Idioma |
|---|---|---|
| dc.contributor.author | Oliveira, Miguel | - |
| dc.contributor.author | Silva, Pedro | - |
| dc.contributor.author | Santos, Cristina | - |
| dc.contributor.author | Costa, L. | - |
| dc.date.accessioned | 2013-12-06T14:40:30Z | - |
| dc.date.available | 2013-12-06T14:40:30Z | - |
| dc.date.issued | 2013-07-06 | - |
| dc.identifier.isbn | 9781450319645 | por |
| dc.identifier.uri | https://hdl.handle.net/1822/26781 | - |
| dc.description.abstract | This paper describes the analysis of a crawl gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. In order to optimize the crawl gait, a multiobjective problem, an optimization system based on NSGAII allows to find a set of non-dominated solutions that correspond to different motor solutions The experimental results highlight the effectiveness of this multi-objective approach. | por |
| dc.description.sponsorship | (undefined) | por |
| dc.language.iso | eng | por |
| dc.publisher | IEEE | por |
| dc.rights | openAccess | por |
| dc.subject | Evolutionary robotics | por |
| dc.subject | Multi-objective optimization | por |
| dc.subject | Genetic | por |
| dc.subject | Genetic algorithms | por |
| dc.title | Sensitivity analysis of a crawl gait multi-objective optimization system | por |
| dc.type | conferencePaper | por |
| dc.peerreviewed | yes | por |
| sdum.publicationstatus | published | por |
| oaire.citationStartPage | 35 | por |
| oaire.citationEndPage | 36 | por |
| oaire.citationTitle | Genetic and Evolutionary Compotation Conference 2013 | por |
| dc.identifier.doi | 10.1145/2464576.2464592 | por |
| sdum.conferencePublication | Genetic and Evolutionary Compotation Conference 2013 | por |
| Aparece nas coleções: | ||
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| GECCO2013_poster_cs.pdf | 118,4 kB | Adobe PDF | Ver/Abrir |
