A statistical comparison of grammatical evolution strategies in the domain of human genetics

Author

White, Bill C. ; Reif, David M. ; Gilbert, Joshua C. ; Moore, Jason H.

Author_Institution

Dept. of Genetics, Dartmouth Med. Sch., Lebanon, NH

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

491

Abstract

Detecting and characterizing genetic predictors of human disease susceptibility is an important goal in human genetics. New chip-based technologies are available that facilitate the measurement of thousands of DNA sequence variations across the human genome. Biologically-inspired stochastic search algorithms are expected to play an important role in the analysis of these high-dimensional datasets. We simulated datasets with up to 6000 attributes using two different genetic models and statistically compared the performance of grammatical evolution, grammatical swarm, and random search for building symbolic discriminant functions. We found no statistical difference among search algorithms within this specific domain

Keywords

DNA; diseases; genetics; search problems; statistical analysis; stochastic processes; DNA sequence; biologically-inspired stochastic search algorithm; chip-based technology; grammatical evolution strategy; grammatical swarm; human disease; human genetics; random search algorithm; statistical analysis; symbolic discriminant function; Bioinformatics; Biological system modeling; DNA; Diseases; Evolution (biology); Genetics; Genomics; Humans; Semiconductor device measurement; Sequences;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Conference_Location

Edinburgh, Scotland

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554723

Filename

1554723