Name
Abstract | ||
|---|---|---|
In this paper, we propose an estimation of distribution algorithm based on an inexpensive Gaussian mixture model with online learning, which will be employed in dynamic optimization. Here, the mixture model stores a vector of sufficient statistics of the best solutions, which is subsequently used to obtain the parameters of the Gaussian components. This approach is able to incorporate into the current mixture model potentially relevant information of the previous and current iterations. The online nature of the proposal is desirable in the context of dynamic optimization, where prompt reaction to new scenarios should be promoted. To analyze the performance of our proposal, a set of dynamic optimization problems in continuous domains was considered with distinct levels of complexity, and the obtained results were compared to the results produced by other existing algorithms in the dynamic optimization literature. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CEC.2011.5949598 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
Gaussian processes,evolutionary computation,learning (artificial intelligence),continuous domains,distribution algorithm,dynamic optimization,evolutionary algorithm,inexpensive Gaussian mixture model,online learning,Online learning,estimation of distribution algorithms,mixture model,optimization in dynamic environments | Mathematical optimization,Evolutionary algorithm,Estimation of distribution algorithm,Computer science,Evolutionary computation,Gaussian,Artificial intelligence,Gaussian process,Probability density function,Optimization problem,Mixture model,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Ricardo Gonçalves | 1 | 16 | 6.43 |
| Fernando J. Von Zuben | 2 | 831 | 81.83 |