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

Authors:
Kay C. Wiese;Andrew Hendriks;Alain Deschênes;Belgacem Ben Youssef
Affiliations:
Simon Fraser University, Surrey, B.C., Canada;Simon Fraser University, Surrey, B.C., Canada;Simon Fraser University, Surrey, B.C., Canada;Simon Fraser University, Surrey, B.C., Canada
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 4
Cited 0

An exhaustive analysis of multiplicative congruential random number generators with modulus 231-1

SIAM Journal on Scientific and Statistical Computing
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems

Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Efficient and Accurate Parallel Genetic Algorithms

Efficient and Accurate Parallel Genetic Algorithms
On Random Numbers And The Performance Of Genetic Algorithms

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.