Name
Title | ||
|---|---|---|
Introducing MONEDA: scalable multiobjective optimization with a neural estimation of distribution algorithm |
Abstract | ||
|---|---|---|
In this paper we explore the model-building issue of multiobjective optimization estimation of distribution algorithms. We argue that model-building has some characteristics that differentiate it from other machine learning tasks. A novel algorithm called multiobjective neural estimation of distribution algorithm (MONEDA) is proposed to meet those characteristics. This algorithm uses a custom version of the growing neural gas (GNG) network specially meant for the model-building task. As part of this work, MONEDA is assessed with regard to other classical and state-of-the-art evolutionary multiobjective optimizers when solving some community accepted test problems. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1389095.1389230 | GECCO |
Keywords | Field | DocType |
model-building issue,introducing moneda,scalable multiobjective optimization,state-of-the-art evolutionary multiobjective optimizers,model-building task,multiobjective neural estimation,neural gas,novel algorithm,custom version,multiobjective optimization estimation,distribution algorithm,test problem,estimation of distribution algorithm,machine learning,multiobjective optimization,model building | Mathematical optimization,Estimation of distribution algorithm,Computer science,Multi-objective optimization,Artificial intelligence,Machine learning,Neural gas,Scalability | Conference |
Citations | PageRank | References |
16 | 0.72 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Martí | 1 | 100 | 7.73 |
| Jesús García | 2 | 31 | 1.97 |
| Antonio Berlanga | 3 | 16 | 0.72 |
| José Manuel Molina | 4 | 16 | 0.72 |