Name
Title | ||
|---|---|---|
Advancing model–building for many–objective optimization estimation of distribution algorithms |
Abstract | ||
|---|---|---|
In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model–building algorithms. Most current model–building schemes used so far off–the–shelf machine learning methods. These methods are mostly error–based learning algorithms. However, the model–building problem has specific requirements that those methods do not meet and even avoid. In this work we dissect this issue and propose a set of algorithms that can be used to bridge the gap of MOEDA application. A set of experiments are carried out in order to sustain our assertions. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12239-2_53 | EvoApplications (1) |
Keywords | Field | DocType |
moeda application,specific requirement,substantial improvement,current model,building problem,objective optimization estimation,distribution algorithm,shelf machine,model building,optimal estimation,machine learning,distributed algorithm | Online machine learning,Mathematical optimization,Stability (learning theory),Active learning (machine learning),Estimation of distribution algorithm,Computer science,Model building,CMA-ES,Artificial intelligence,Computational learning theory,Machine learning,Weighted Majority Algorithm | Conference |
ISBN | Citations | PageRank |
3-642-12238-8 | 0 | 0.34 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Martí | 1 | 100 | 7.73 |
| Jesús García | 2 | 44 | 3.95 |
| Antonio Berlanga | 3 | 0 | 0.34 |
| José M. Molina | 4 | 604 | 67.82 |