CoG kits: a bridge between commodity distributed computing and high-performance grids
Proceedings of the ACM 2000 conference on Java Grande
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inference of a gene regulatory network by means of interactive evolutionary computing
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy
Proceedings of the 3rd International Conference on Genetic Algorithms
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
The superstructure toward open bioinformatics grid
New Generation Computing - Grid systems for life sciences
A grid-oriented genetic algorithm framework for bioinformatics
New Generation Computing - Grid systems for life sciences
Fault-tolerant network computation of individuals in genetic algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
DNA fragment assembly using a grid-based genetic algorithm
Computers and Operations Research
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This paper presents a genetic algorithm running on a grid computing environment for inference of genetic networks. In bioinformatics, inference of genetic networks is one of the most important problems, in which mutual interactions among genes are estimated by using gene-expression time-course data. Network-Structure-Search Evolutionary Algorithm (NSS-EA) is a promising inference method of genetic networks that employs S-system as a model of genetic network and a genetic algorithm (GA) as a search engine. In this paper, we propose an implementation of NSS-EA running on a multi-PC-cluster grid computing environment where multiple PC clusters are connected over the Internet. We “Gridifiy” NSS-EA by using a framework for the development of GAs running on a multi-PC-cluster grid environment, named Grid-Oriented Genetic Algorithm Framework (GOGA Framework). We examined whether the “Gridified” NSS-EA works correctly and evaluated its performance on Open Bioinformatics Grid (OBIGrid) in Japan.