Distribution-based adaptive bounding genetic algorithm for continuous optimisation problems

Jian-Xun Peng, Kang Li, Steve Thompson, Peter A. Wieringa

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


A new search-space-updating technique for genetic algorithms is proposed for continuous optimisation problems. Other than gradually reducing the search space during the evolution process with a fixed reduction rate set ‘a priori’, the upper and the lower boundaries for each variable in the objective function are dynamically adjusted based on its distribution statistics. To test the effectiveness, the technique is applied to a number of benchmark optimisation problems in comparison with three other techniques, namely the genetic algorithms with parameter space size adjustment (GAPSSA) technique [A.B. Djurišic, Elite genetic algorithms with adaptive mutations for solving continuous optimization problems – application to modeling of the optical constants of solids, Optics Communications 151 (1998) 147–159], successive zooming genetic algorithm (SZGA) [Y. Kwon, S. Kwon, S. Jin, J. Kim, Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems, Computers and Structures 81 (2003) 1715–1725] and a simple GA. The tests show that for well-posed problems, existing search space updating techniques perform well in terms of convergence speed and solution precision however, for some ill-posed problems these techniques are statistically inferior to a simple GA. All the tests show that the proposed new search space update technique is statistically superior to its counterparts.
Original languageEnglish
Pages (from-to)1063-1077
Number of pages15
JournalApplied Mathematics and Computation
Issue number2
Publication statusPublished - 15 Feb 2007

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics
  • Numerical Analysis


Dive into the research topics of 'Distribution-based adaptive bounding genetic algorithm for continuous optimisation problems'. Together they form a unique fingerprint.

Cite this