- 11 readers
- Pages displayed : 221836
- Unique visitors : 78556
- Pages displayed in last 24 hours : 21
- Unique visitors in last 24 hours : 16
Tag Archives: Data-intensive computing
Below you may find the slides of the GECCO 2009 tutorial that Jaume Bacardit and I put together. Hope you enjoy it.
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes […]
- Observer-Invariant Histopathology using Genetics-Based Machine Learning
- Deadline extended for special issue on Metaheuristics for Large Scale Data Mining
- [BDCSG2008] Algorithmic Perspectives on Large-Scale Social Network Data (Jon Kleinberg)
Below you may find the slides I used during GECCO 2009 to present the paper titled “Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre”. An early preprint in form of technical report can be found as an IlliGAL TR No. 2009001 or the full paper at the ACM digital library
- Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre
- Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
- Scaling Genetic Algorithms using MapReduce
I am currently working on the distributed execution of flows as part of the Meandre infrastructure—as a part of the SEASR project. One of the pieces to explore is how to push data between machines. No, I am not going to talk about network protocols and the like here, but how you can pass the […] Continue reading
byÂ LlorÃ , X.
IlliGAL technical report 2009001.Â You can download the pdfÂ here. More information is also available at the Meandre websiteÂ as part of theÂ SEASR project.
Abstract:Â 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 […] Continue reading
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 […] Continue reading