Name
Title | ||
|---|---|---|
Learning Structure Illuminates Black Boxes - An Introduction to Estimation of Distribution Algorithms |
Abstract | ||
|---|---|---|
This chapter serves as an introduction to estimation of distribution algorithms (EDAs). Estimation of distribution algorithms are a new paradigm in evolutionary computation. They combine statistical learning with population-based search in order to automatically identify and exploit certain structural properties of optimization problems. State-of-the-art EDAs consistently outperform classical genetic algorithms on a broad range of hard optimization problems. We review fundamental terms, concepts, and algorithms which facilitate the understanding of EDA research. The focus is on EDAs for combinatorial and continuous non-linear optimization and the major differences between the two fields are discussed. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-72960-0_18 | Natural Computing Series |
Keywords | Field | DocType |
Black Box Optimization,Probabilistic Models,Estimation of Distributions | EDAS,Population,Estimation of distribution algorithm,Evolutionary computation,Theoretical computer science,Artificial intelligence,Quality control and genetic algorithms,Optimization problem,Machine learning,Genetic algorithm,Mathematics,Metaheuristic | Conference |
ISSN | Citations | PageRank |
1619-7127 | 1 | 0.34 |
References | Authors | |
44 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jörn Grahl | 1 | 194 | 15.68 |
| Stefan Minner | 2 | 362 | 41.63 |
| Peter A. N. Bosman | 3 | 507 | 49.04 |