Name
Title | ||
|---|---|---|
Latent variable model for estimation of distribution algorithm based on a probabilistic context-free grammar |
Abstract | ||
|---|---|---|
Estimation of distribution algorithms are evolutionary algorithms using probabilistic techniques instead of traditional genetic operators. Recently, the application of probabilistic techniques to program and function evolution has received increasing attention, and this approach promises to provide a strong alternative to the traditional genetic programming techniques. Although a probabilistic context-free grammar (PCFG) is a widely used model for probabilistic program evolution, a conventional PCFG is not suitable for estimating interactions among nodes because of the context freedom assumption. In this paper, we have proposed a new evolutionary algorithm named programming with annotated grammar estimation based on a PCFG with latent annotations, which allows this context freedom assumption to be weakened. By applying the proposed algorithm to several computational problems, it is demonstrated that our approach is markedly more effective at estimating building blocks than prior approaches. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TEVC.2009.2015574 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
probabilistic technique,new evolutionary algorithm,function evolution,annotated grammar estimation,latent variable model,conventional pcfg,evolutionary algorithm,probabilistic program evolution,distribution algorithm,probabilistic context-free grammar,context freedom assumption,ant colony optimization,probability,genetic algorithms,sampling methods,predictive models,bayesian methods,genetic operator,evolutionary computation,genetic programming,context modeling,estimation of distribution algorithm,em algorithm | Mathematical optimization,Evolutionary algorithm,Estimation of distribution algorithm,Computer science,Evolutionary computation,Genetic programming,Probabilistic analysis of algorithms,Artificial intelligence,Probabilistic logic,Probabilistic relevance model,Stochastic grammar,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 4 | 1089-778X |
Citations | PageRank | References |
13 | 0.71 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshihiko Hasegawa | 1 | 28 | 4.05 |
| Hitoshi Iba | 2 | 1541 | 138.51 |