The impact of pseudorandom number quality on P-RnaPredict, a parallel genetic algorithm for RNA secondary structure' prediction

被引：0
|
作者：
Wiese, Kay C. [1 ]
Hendriks, Andrew [1 ]
Deschnes, Alain [1 ]
Ben Youssef, Belgacern [1 ]
机构：
[1] Simon Fraser Univ, Sch Comp Sci, Surrey, England
来源：
GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2 | 2005年
关键词：
bioinformatics; RNA secondary structure prediction; parallel evolutionary algorithms; random number generators;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented. The PRNG comparison tests were performed with known structures that are 118, 122, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures.
引用
收藏
页码：479 / 480
页数：2
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