Estimation of distribution algorithm based on probabilistic grammar with latent annotations

Genetic Programming (GP) which mimics the natural evolution to optimize functions and programs, has been applied to many problems. In recent years, evolutionary algorithms are seen from the viewpoint of the estimation of distribution. Many algorithms called EDAs (Estimation of Distribution Algorithm...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2007 IEEE Congress on Evolutionary Computation S. 1043 - 1050
Hauptverfasser: Hasegawa, Y., Iba, H.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2007
Schlagworte:
Biological cells
Electronic design automation and methodology
Evolutionary computation
Genetic algorithms
Genetic programming
Informatics
Power engineering and energy
Probability
Production
Robots
ISBN:1424413397, 9781424413393
ISSN:1089-778X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Genetic Programming (GP) which mimics the natural evolution to optimize functions and programs, has been applied to many problems. In recent years, evolutionary algorithms are seen from the viewpoint of the estimation of distribution. Many algorithms called EDAs (Estimation of Distribution Algorithms) based on probabilistic techniques have been proposed. Although probabilistic context free grammar (PCFG) is often used for the function and program evolution, it assumes the independence among the production rules. With this simple PCFG, it is not able to induce the building-blocks from promising solutions. We have proposed a new function evolution algorithm based on PCFG using latent annotations which weaken the independence assumption. Computational experiments on two subjects (the royal tree problem and the DMAX problem) demonstrate that our new approach is highly effective compared to prior approaches.
ISBN:1424413397
9781424413393
ISSN:1089-778X
DOI:10.1109/CEC.2007.4424585