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. ...

Designing the distributed execution of a generic Meandre flow involves several moving pieces. One of those is the temporary storage required by the computing nodes (think of it as one node as one isolated component of a flow) to keep up with the data generated by a component, and also be able to replicate such storage to the node containing the consumer to be fed. Such storage, local to each node, must guarantee at least three basic properties. ...

A while ago I wrote some thoughts about how to map RDF to and from FluidDB. There I explored how you could map RDF onto FluidDB, and how to get it back. That got me thinking about how to get a simple endpoint you could query for RDF. Imagine that you could pull FluidDB data in RDF, then I could just get all the flexibility of SPARQL for free. With this idea in my mind I just went and grabbed Meandre, the JFLuidDB library started by Ross Jones, and build a few components. The main goal was to be able to get an object, list of the tags, and express the result in RDF. FluidDB helps the mapping since objects are uniquely identified by URIs. For instance, the unique object 5ff74371-455b-4299-83f9-ba13ae898ad1 (FluidDB relies on UUID version four with the form xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx) is uniquely identified by http://sandbox.fluidinfo.com/objects/5ff74371-455b-4299-83f9-ba13ae898ad1 (or a url of the form http://sandbox.fluidinfo.com/objects/xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx), in case you are using the sandbox or http://fluiddb.fluidinfo.com/objects/5ff74371-455b-4299-83f9-ba13ae898ad1 if you are using the main instance. Same story for tags. The tag fluiddb/about can be uniquely identified by the URI http://sandbox.fluidinfo.com/tags/fluiddb/about, or http://fluiddb.fluidinfo.com/tags/fluiddb/about. ...

Yesterday I was visiting Monmouth College to participate on the Darwinpalooza which commemorates the 200th anniversary of Charles Darwin’s birth and the 150th anniversary of the publication of On the Origin of Species. After scratching my head about about what to present, I came out with quite a mix. You will find the abstract of the talk below, as well as the slides I used. Abstract: One hundred and fifty years have passed since the publication of Darwin’s world-changing manuscript “The Origins of Species by Means of Natural Selection”. Darwin’s ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin’s ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems. ...

Meandre (NCSA pushed data-intensive computing infrastructure) relies on RDF to describe components, flows, locations and repositories. RDF has become the central piece that makes possible Meandre’s flexibility and reusability. However, one piece still remains largely sketchy and still has no clear optimal solution: How can we facilitate to anybody sharing, publishing and annotating flows, components, locations and repositories? More importantly, how can that be done in the cloud in an open-ended fashion and allow anybody to annotate and comment on each of the afore mentioned pieces? ...