Optimization Letters

, Volume 2, Issue 3, pp 433–443 | Cite as

Extending a class of continuous estimation of distribution algorithms to dynamic problems

  • Bo Yuan
  • Maria Orlowska
  • Shazia Sadiq
Original Paper


In this paper, a class of continuous Estimation of Distribution Algorithms (EDAs) based on Gaussian models is analyzed to investigate their potential for solving dynamic optimization problems where the global optima may change dramatically during time. Experimental results on a number of dynamic problems show that the proposed strategy for dynamic optimization can significantly improve the performance of the original EDAs and the optimal solutions can be consistently located.


Evolutionary algorithms Global optimization Estimation of distribution algorithms Dynamic optimization 


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Division of Informatics, Graduate School at ShenzhenTsinghua UniversityShenzhenPeople’s Republic of China
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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