Abstract:
This paper shows how the extended compact genetic algorithm can be scaled using data-intensive computing techniques such as MapReduce. Two different frameworks (Hadoop and MongoDB) are used to deploy MapReduce implementations of the compact and extended com- pact genetic algorithms. Results show that both are good choices to deal with large-scale problems as they can scale with the number of commodity machines, as opposed to previous ef- forts with other techniques that either required specialized high-performance hardware or shared memory environments.

Related posts:

1. Scaling Genetic Algorithms using MapReduce
2. Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre
3. Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre

by Llorà, X.

IlliGAL technical report 2009001. You can download the pdf here. More information is also available at the Meandre website as part of the SEASR project.

Abstract: Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases—selectorecombinative genetic algorithms and estimation of distribution algorithms—are presented, analyzed, discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.

Related posts:

1. Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre
2. Analyzing active interactive genetic algorithms using visual analytics
3. Scaling eCGA Model Building via Data-Intensive Computing