An adaptive sampling scheme for genetic algorithms on the sampled onemax problem

Tian Li Yu*, Ying-ping Chen, David E. Goldberg, Jian Hung Chen

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

This paper proposes an adaptive sampling scheme for genetic algorithms. The adaptive sampling scheme is tested on the sampled OneMax problem. The results suggest that through this scheme, speed-up is obtained for problems with non-uniformly scaled building blocks (BBs). For problems with uniformly scaled BBs, the proposed adaptive sampling scheme does not give speed-up but still maintains the same performance with respect to the number of function evaluations when the adaptive sampling scheme is not adopted.

Original languageEnglish
Pages39-44
Number of pages6
StatePublished - 1 Dec 2003
EventSmart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life - Proceedings of the Artificial Neural Networks in Engineering Conference - St. Louis, MO., United States
Duration: 2 Nov 20035 Nov 2003

Conference

ConferenceSmart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life - Proceedings of the Artificial Neural Networks in Engineering Conference
CountryUnited States
CitySt. Louis, MO.
Period2/11/035/11/03

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