Parameter-less Optimization with the Extended Compact Genetic Algorithm and Iterated Local Search
| dc.creator | Lima, Claudio F. | |
| dc.creator | Lobo, Fernando G. | |
| dc.date | 2004-02-19 | |
| dc.date.accessioned | 2026-07-07T03:20:56Z | |
| dc.date.available | 2026-07-07T03:20:56Z | |
| dc.description | This paper presents a parameter-less optimization framework that uses the extended compact genetic algorithm (ECGA) and iterated local search (ILS), but is not restricted to these algorithms. The presented optimization algorithm (ILS+ECGA) comes as an extension of the parameter-less genetic algorithm (GA), where the parameters of a selecto-recombinative GA are eliminated. The approach that we propose is tested on several well known problems. In the absence of domain knowledge, it is shown that ILS+ECGA is a robust and easy-to-use optimization method. | |
| dc.description | 12 pages, submitted to gecco 2004 | |
| dc.identifier | https://arxiv.org/abs/cs/0402047 | |
| dc.identifier | http://arxiv.org/abs/cs/0402047 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32010 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.subject | G.1.6; I.2.6; I.2.8 | |
| dc.title | Parameter-less Optimization with the Extended Compact Genetic Algorithm and Iterated Local Search | |
| dc.type | text |