DocumentCode :
2540055
Title :
Optimisation using population based incremental learning (PBIL)
Author :
Hughes, Evan J.
Author_Institution :
Dept. of Aerosp., Power & Sensors, Cranfield Univ., Shrivenham, UK
fYear :
1998
fDate :
36109
Firstpage :
42401
Lastpage :
42403
Abstract :
The population-based incremental learning (PBIL) algorithm is a simple stochastic optimisation technique that can be applied quickly to a wide range of problems. The technique´s main area of application is for problems that are too multimodal or discontinuous for gradient or simplex methods, but don´t warrant a full evolutionary algorithm solution. PBIL has been shown to outperform conventional deterministic and stochastic optimisation techniques on a wide range of problems and yet is simple to code. This paper describes a practical approach to applying the PBIL algorithm to optimisation problems. First the operation of the algorithm is described and then guidelines for tuning the algorithm are presented. An example implementation is given and a method for reducing the processing burden of the algorithm is detailed. An example MATLAB routine is included to demonstrate the simplicity of the algorithm
Keywords :
stochastic programming; MATLAB routine; PBIL; discontinuous problems; multimodal problems; population-based incremental learning; stochastic optimisation;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Optimisation in Control: Methods and Applications (Ref. No. 1998/521), IEE Colloquium on
Conference_Location :
London
Type :
conf
DOI :
10.1049/ic:19981063
Filename :
744258
Link To Document :
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