Simplex genetic algorithm hybrid

John Yen, Bogju Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations


One of the main obstacles in applying genetic algorithms (GAs) to complex problems has been the high computational cost due to their slow convergence rate. To alleviate this difficulty, we developed a hybrid approach that combines GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conventional simplex method. In an attempt to make effective use of the simplex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ranked population. We compared our approach with five alternative optimization techniques including a simplex-GA hybrid independently developed by Renders and Bersini and Adaptive Simulated Annealing (ASA). We used two function optimization problems to compare our approach with the five alternative methods. Overall, these tests showed that our hybrid approach is an effective and robust optimization technique. We also tested our hybrid GA on the seven function benchmark problems on real space and showed the results.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation, ICEC
Editors Anon
Number of pages6
StatePublished - 1997
EventProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97 - Indianapolis, IN, USA
Duration: Apr 13 1997Apr 16 1997


OtherProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97
CityIndianapolis, IN, USA

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering


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