Paper Info
Title
DE/EDA: A new evolutionary algorithm for global optimization.
Abstract
Differential evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. Estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a combination of DE and EDA (DE/EDA) for the global continuous optimization problem. DE/EDA combines global information extracted by EDA with differential information obtained by DE to create promising solutions. DE/EDA has been compared with the best version of the DE algorithm and an EDA on several commonly utilized test problems. Experimental results demonstrate that DE/EDA outperforms the DE algorithm and the EDA. The effect of the parameters of DE/EDA to its performance is investigated experimentally.
Year
DOI
Venue
2005
10.1016/j.ins.2004.06.009
Inf. Sci.
Keywords
Field
DocType
global optimization,direction information,new evolutionary algorithm,global continuous optimization problem,utilized test problem,differential evolution,global information,distribution algorithm,best version,promising solution,differential information,de algorithm,estimation of distribution algorithm,continuous optimization,evolutionary algorithm,information extraction
Population,Mathematical optimization,Probability model,Estimation of distribution algorithm,Global optimization,Evolutionary algorithm,Global information,Continuous optimization problem,Algorithm,Differential evolution,Mathematics
Journal
Volume
Issue
ISSN
169
3-4
0020-0255
Citations 
PageRank 
References 
105
7.21
2
Authors
3
Search Limit
100105
Name
Order
Citations
PageRank
Jianyong Sun145736.37
Qingfu Zhang27634255.05
Edward P. K. Tsang389987.77