Estimation of Distribution Algorithms

I just uploaded the technical report of the paper we put together for CEC 2010 on how we can scale up eCGA using a MapReduce approach. The paper, besides exploring the Hadoop implementation, it also presents some very compelling results obtained with MongoDB (a document based store able to perform parallel MapReduce tasks via sharding). The paper is available as PDF. Technical report 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. ...

If you are planning to submit a paper for the 2010 Genetic and Evolutionary Computation Conference, the deadline is January 13, 2010 (and now extended to January 27th). You can find more information at the GECCO 2010 calendar site.

Below you may find the abstract to and the link to the technical report of the paper entitled “Scaling Genetic Algorithms using MapReduce” that will be presented at the Ninth International Conference on Intelligent Systems Design and Applications (ISDA) 2009 by Verma, A., Llorà, X., Campbell, R.H., Goldberg, D.E. next month. Abstract: Genetic algorithms(GAs) are increasingly being applied to large scale problems. The traditional MPI-based parallel GAs do not scale very well. MapReduce is a powerful abstraction developed by Google for making scalable and fault tolerant applications. In this paper, we mould genetic algorithms into the the MapReduce model. We describe the algorithm design and implementation of GAs on Hadoop, the open source implementation of MapReduce. Our experiments demonstrate the convergence and scalability upto 105 variable problems. Adding more resources would enable us to solve even larger problems without any changes in the algorithms and implementation. The draft of the paper can be downloaded as IlliGAL TR. No. 2009007. ...

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

Martin Pelikan just posted on the MEDAL and IlliGAL blogs that the GECCO 2009 paper submission deadline has been extended till January 28. Always, great news to finish polishing last minute changes :D It seems that the website is not yet updated, neither emails sent to the usual mailing lists. Just a matter or time I guess ;) And since I am talking about GECCO 2009, you may also want to take a look at the competitions announcement video that Pier Luca Lanzi put together below. httpv://www.youtube.com/watch?v=i7cbgBOkn-s ...