Name
Abstract | ||
|---|---|---|
Estimation of Distribution Algorithm (EDA) is a novel evolutionary computation, which mainly depends on learning and sampling mechanisms to manipulate the evolutionary search, and has been proved a potential technique for complex problems. However, EDA generally spend too much time on the learning about the probability distribution of the promising individuals. The paper propose an improved EDA based on copula theory (copula-EDA) to enhance the learning efficiency, which models and samples the joint probability function by selecting a proper copula and learning the marginal probability distributions of the promising population. The simulating results prove the approach is easy to implement and is validated on several problems. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-12834-9_7 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
distribution algorithm,sampling methods,distribution functions,estimation,np complete problem,gaussian distribution,probability distribution,data mining,statistical distributions,evolutionary computing,production,estimation of distribution algorithm,computational intelligence,probability function,evolutionary computation | Mathematical optimization,Joint probability distribution,Estimation of distribution algorithm,Computer science,Copula (linguistics),Copula (probability theory),Evolutionary computation,Probability distribution,Artificial intelligence,Probability density function,Marginal distribution,Machine learning | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4244-2959-2 | 21 | 1.36 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lifang Wang | 1 | 24 | 2.85 |
| Jianchao Zeng | 2 | 930 | 94.89 |
| Yi Hong | 3 | 621 | 45.07 |