Name
Title | ||
|---|---|---|
On the Computational Properties of the Multi-Objective Neural Estimation of Distribution Algorithm |
Abstract | ||
|---|---|---|
This paper explores the behavior of the multi-objective neural EDA (MONEDA) in terms of its computational requirements it demands and assesses how it scales when dealing with multi-objective optimization problems with relatively large amounts of objectives. In order to properly comprehend these matters other MOEDAs and MOEAs are included in the analysis. The experiments performed tested the ability of each approach to scalably solve many-objective optimization problems. The fundamental result obtained is that MONEDA is not only yields similar or better solutions when compared with other approaches but also does it with at a lower computational cost. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-03211-0_20 | Studies in Computational Intelligence |
Keywords | Field | DocType |
Multi-objective Optimization,Computational Complexity,Multi-objective Optimization Evolutionary Algorithms,Estimation of Distribution Algorithms (EDAs) | Continuous optimization,Mathematical optimization,Vector optimization,Computer science,Meta-optimization,Test functions for optimization,Multi-objective optimization,Multi-swarm optimization,Random optimization,Optimization problem | Conference |
Volume | ISSN | Citations |
236 | 1860-949X | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Martí | 1 | 100 | 7.73 |
| Jesús García | 2 | 238 | 30.37 |
| Antonio Berlanga | 3 | 196 | 23.09 |
| José M. Molina | 4 | 604 | 67.82 |