Xavier Llorà » Estimation of Distribution Algorithms

Parallel and Distributed Computational Intelligence book is out for pre-order

Xavier — Tue, 14 Sep 2010 04:17:35 +0000

“Parallel and Distributed Computational Intelligence” edited by Francisco Fernández de Vega & Erick Cantú-Paz and published by Springer is out for pre-order. The first chapter “When Huge is Routine: Scaling Genetic Algorithms and Estimation of Distribution Algorithms via Data-Intensive Computing” of the book was written together with coauthors Abhishek Verma, Roy Campbell, and David E. Goldberg describing how data-intensive computing can help push the size of problems that GAs and EDAs can address. You may find the abstact of the book below.

Abstract:

The growing success of biologically inspired algorithms in solving large and complex problems has spawned many interesting areas of research. Over the years, one of the mainstays in bio-inspired research has been the exploitation of parallel and distributed environments to speedup computations and to enrich the algorithms. From the early days of research on bio-inspired algorithms, their inherently parallel nature was recognized and different parallelization approaches have been explored. Parallel algorithms promise reductions in execution time and open the door to solve increasingly larger problems. But parallel platforms also inspire new bio-inspired parallel algorithms that, while similar to their sequential counterparts, explore search spaces differently and offer improvements in solution quality.

The objective in editing this book was to assemble a sample of the best work in parallel and distributed biologically inspired algorithms. The editors invited researchers in different domains to submit their work. They aimed to include diverse topics to appeal to a wide audience. Some of the chapters summarize work that has been ongoing for several years, while others describe more recent exploratory work. Collectively, these works offer a global snapshot of the most recent efforts of bioinspired algorithms’ researchers aiming at profiting from parallel and distributed computer architectures—including GPUs, Clusters, Grids, volunteer computing and p2p networks as well as multi-core processors. This volume will be of value to a wide set of readers, including, but not limited to specialists in Bioinspired Algorithms, Parallel and Distributed Computing, as well as computer science students trying to figure out new paths towards the future of computational intelligence.

Scaling eCGA Model Building via Data-Intensive Computing

Xavier — Thu, 08 Apr 2010 16:17:39 +0000

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 and PS.

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.

Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre

Xavier — Tue, 14 Jul 2009 04:15:51 +0000

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