Hybridizing cultural algorithms and local search

Trung Thanh NGUYEN, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

18 Citations (Scopus)


In this paper, we propose a new population-based framework for combining local search with global explorations to solve single-objective unconstrained numerical optimization problems. The idea is to use knowledge about local optima found during the search to a) locate promising regions in the search space and b) identify suitable step sizes to move from one optimum to others in each region. The search knowledge was maintained using a Cultural Algorithm-based structure, which is updated by behaviors of individuals and is used to actively guide the search. Some experiments have been carried out to evaluate the performance of the algorithm on well-known continuous problems. The test results show that the algorithm can get comparable or superior results to that of some current well-known unconstrained numerical optimization algorithms in certain classes of problems. © Springer-Verlag Berlin Heidelberg 2006.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning : IDEAL 2006 : 7th International Conference, Burgos, Spain, September 20-23, 2006, Proceedings
EditorsEmilio CORCHADO, Hujun YIN, Vicente BOTTI, Colin FYFE
PublisherSpringer Berlin Heidelberg
Number of pages9
ISBN (Electronic)9783540454878
ISBN (Print)9783540454854
Publication statusPublished - 2006
Externally publishedYes
Event7th International Conference on Intelligent Data Engineering and Automated Learning - Burgos, Spain
Duration: 20 Sept 200623 Sept 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Intelligent Data Engineering and Automated Learning


  • Brent direct search
  • Cultural algorithm
  • Evolutionary algorithm
  • Iterated local search
  • Unconstrained numerical optimization


Dive into the research topics of 'Hybridizing cultural algorithms and local search'. Together they form a unique fingerprint.

Cite this