A distributed genetic algorithm for RNA secondary structure prediction

Hendriks, A.; Wiese, Kay C.; Glen, Edward; Deschenes, A.

doi:10.1109/cec.2003.1299596

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…e dCMA-ES preserves diversity in the population through multiple demes, while increasing the selection pressure through periodic migration [47]. e migration operator includes four parameters: (a) migration topology that defines the topology of the connections between demes, (b) the migration rate (the fraction of the population that migrates) that controls how many individuals migrate, (c) migration interval that affects the frequency of migrations, and (d) migration policy that selects emigrants and replaces existing individuals with incoming migrants [48].…”

Section: Distributed Covariance Matrix Adaptation Evolution Strategy Direct Algorithm (Dcma-es-direct)mentioning

confidence: 99%

A Globally Optimal Robust Design Method for Complex Systems

Chen

Shi

2020

Complexity

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The uncertainty of the engineering system increases with the growing complexity of the engineering system; therefore, the tolerance to the uncertainty is essential. In the design phase, the output performance should reach the design criterion, even under large variations of design parameters. The tolerance to design parameter variations may be measured by the size of a solution space in which the output performance is guaranteed to deliver the required performance. In order to decouple dimensions, a maximum solution hyperbox, expressed by intervals with respect to each design parameter, is sought. The proposed approach combines the metaheuristic algorithm with the DIRECT algorithm where the former is used to seek the maximum size of hyperbox, and the latter is used as a checking technique that guarantees the obtained hyperbox is indeed a solution hyperbox. There are three advantages of the proposed approach. First, it is a global search and has a considerable high possibility to produce the globally maximum solution hyperbox. Second, it can be used for both analytically known and black-box performance functions. Third, it guarantees that any point selected within the obtained hyperbox satisfies the performance criterion as long as the performance function is continuous. Furthermore, the proposed approach is illustrated by numerical examples and real examples of complex systems. Results show that the proposed approach outperforms the GHZ and CES-IA methods in the literature.

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Section: Distributed Covariance Matrix Adaptation Evolution Strategy Direct Algorithm (Dcma-es-direct)mentioning

confidence: 99%

A Globally Optimal Robust Design Method for Complex Systems

Chen

Shi

2020

Complexity

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“…Initialize random population Evaluate fitness of individuals For all NUM GENERATIONS For all DEME COUNT For all DEME SIZE Reproduce by crossover in deme Mutate Employ replacement (STDS or KBR) Apply Elitism If MIGRATION INTERVAL, migrate Given the enourmous number of possible parameter combinations, the selection of parameter sets for these experi- ments was based on previously published experimental results [11,29]. The parameters specifically relating to the serial GA were chosen based on those which produced the best set of results in [29], and were set as follows: The global population was set to 700, with the crossover probability (P c ) set to 0.7.…”

Section: Methodsmentioning

confidence: 99%

“…We selected the INN-HB thermodynamic model and the CX [24] crossover. The parameters specifically relating to the distributed GA were chosen based on those which produced the best set of results in [11], and were set as follows: the global population was split into two separate sets of deme sizes and deme counts: 50 and 14, and 70 and 10 respectively. The migration interval were fixed at 20 generations, and the migration rate was fixed at 10 percent.…”

Section: Methodsmentioning

confidence: 99%

“…The distributed RnaPredict should improve performance through preserving diversity in the population through multiple demes, while increasing the selection pressure through periodic migration [11]. The parallel implementation(PRnaPredict) will offer a speedup as each deme in the distributed GA will be distributed to its own processor.…”

Section: Algorithmsmentioning

confidence: 99%

“…Later, Deschênes and Wiese [5] have shown that RnaPredict, using two stacking based thermodynamic models, can predict certain RNA secondary structures with very high accuracy and also outperform a dynamic programming algorithm [6]. Our specific work builds on [11,10], in which a serial simulation of a distributed version of RnaPredict was implemented that predicted the secondary structure of RNA molecules from input RNA sequences. The extension that we present here is the redevelopment of the serial simulation of the distributed RnaPredict into a fully parallelized distributed GA based on the MPI standard to run on a 128 node Beowulf cluster.…”

Section: Introductionmentioning

confidence: 99%

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

Wiese

Hendriks

Deschenes

et al. 2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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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.

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Structural Data Mining on Secondary Genomic Structure

Lam¹,

Chan²

Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05)

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A distributed genetic algorithm for RNA secondary structure prediction

Cited by 11 publications

References 14 publications

A Globally Optimal Robust Design Method for Complex Systems

A Globally Optimal Robust Design Method for Complex Systems

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

Structural Data Mining on Secondary Genomic Structure

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