Xavier Llorà » Publications

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 Genetic Algorithms using MapReduce

Xavier — Fri, 09 Oct 2009 15:51:19 +0000

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. For more information see the IlliGAL technical reports web site.

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

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

Xavier — Thu, 29 Jan 2009 19:36:25 +0000

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.

Dusting my Ph.D. thesis off

Xavier — Tue, 30 Dec 2008 10:11:00 +0000

After attending Albert Orriols’s Ph.D. thesis defense, I ended wondering how many of the question I posted in mine have not been solved. The answer, quite a bit. So, I just decided to dig it up, and put it up here. Yes, the thesis was not written in English (in those days my fellowship had some strings attached), but math formulation, graphs, and results are readable in any language Also, GALE was written and documented in english, and is available here.

Abstract:
This thesis combines different ideas provided by machine learning, evolutionary computation (genetics-based machine learning), and artificial life. The aim of this work is to create a data mining model that satisfies certain guidelines. The first one deals with the type of attributes that can be used in the model. The second one focuses on the goal that the model has to independent of the knowledge representation used. Finally, the model must exploit massive parallelism. Artificial Life can be very useful dealing with parallelism models.This thesis combines different ideas provided by machine learning, evolutionary computation (genetics-based machine learning), and artificial life. The aim of this work is to create a data mining model that satisfies certain guidelines. The first one deals with the type of attributes thatcan be used in the model. The second one focuses on the goal that the model has to independent of the knowledge representation used. Finally, the model must exploit massive parallelism. Artificial Life can be very useful dealing with parallelism models.

Scribd

GECCO 2009 submission deadline

Xavier — Mon, 17 Nov 2008 12:01:24 +0000

Yup, that time of year is coming around again. The 2009 Genetic and Evolutionary Computing Conference (GECCO 2009) is going to be held in Montreal, Canada. The paper submission dead line is January 14, 2009. Related posts: GECCO 2009 paper submission deadline extended till January 28 GECCO 2011 Submission Deadline: January 26, 2011 GECCO 2010 [...] Related posts:

Meandre: Semantic-Driven Data-Intensive Flows in the Clouds

Xavier — Sat, 15 Nov 2008 23:34:02 +0000

by Llorà, X., Ács, B., Auvil, L., Capitanu, B., Welge, M.E., Goldberg, D.E. (2008).

This paper has been accepted at the 4th IEEE International Conference on e-Science. An early draft of the paper can be found as IlliGAL technical report 2008013. You can download the pdf here. More information is also available at the Meandre website as part of the SEASR project.

Abstract:

Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters,(2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and sharing components. Banking on virtualization and cloud computing techniques the Meandre infrastructure is able to create and dispose Meandre clusters on demand, being transparent to the final user. This paper also presents a prototype of such clustered infrastructure and some results obtained using it.

Analyzing Trends in the Blogosphere Using Human-Centered Analysis and Visualization Tools

Xavier — Mon, 26 Mar 2007 20:23:28 +0000

by Xavier Llorà, Noriko Imafuji Yasui, David E. Goldberg (2006).
Proceedings of the International Conference on Weblogs and Social Mining (ICWSM 2007). Also as IlliGAL TR. No. 2006026. Link to the PDF.

Abstract
The blogsphere is a valuable source of information. From simple topic analysis in the blogosphere—what’s hot—to harvesting and analyzing valuable market trends—what product and features are suggested—require a tight integration of computer- and human-based analysis capabilities. Computers can easily assist the processing filtering and visualizing relevant and key elements of the blogosphere, but coupling them with human evaluation and reasoning can provide the final steps to connect pieces of relevant information into better description map of the current trends of the blogosphere. An example of the need for such human-centered analysis was David R. Ellis’ film Snakes on a Plane (2006) which failed to properly translate blogosphere discussions into a successful commercial product—as a clear misalignment of both environments the blogsphere and the final targeted market. In this paper, we present some human-centered visualization and analysis tools that can help users to compare and reason synergies and misalignments revolving around a particular topic.

Communication gap management for fertile community

Xavier — Sat, 24 Mar 2007 15:17:35 +0000

by Naohiro Matsumura, David E. Goldberg, and Xavier Llorà (2006).
Journal of Soft Computing, Volume 11 , Issue 8, pp. 791–798, ACM press. Link to the ACM portal.
Initial work also available as IlliGAL TR No 2005001. Link to the PDF.

Abstract
In the paper, we first present an approach to extract social networks from message boards on the Internet. Then we propose communication gaps based on structural features of the social networks as an indicator of understanding the state of communication. After we classify 3,000 social networks into three types of communication, i.e., interactive communication, distributed communication, and soapbox communication, we suggest communication gap management to identify the types of communication, the roles of individuals, and important ties, all of which can be used for drawing up a plan for realizing fertile community.

Toward routine billion-variable optimization using genetic algorithms

Xavier — Thu, 18 Jan 2007 22:32:36 +0000

by Goldberg, D. E., Sastry, K., and Llorà X. (2007).
Complexity, 12(3), 27—29. Link to the PDF. Press release.

The push for better understanding and design of complex systems requires the solution of challenging optimization problems with large numbers of decision variables. This note presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of a genetic algorithm (GA). The problem addressed is a noisy, blind problem over a vector of binary decision variables. Noise is added equaling a tenth of the deterministic objective function variance of the problem, thereby making it difficult for simple hillclimbers to find the optimal solution. The genetic algorithm used – the compact GA – is able to find the optimum in the presence of noise quickly, reliably, and accurately, and the solution scalability follows known convergence theories. These results on noisy problem together with other results on problems involving varying modularity, hierarchy, and overlap foreshadow routine solution of billion-variable problems across the landscape of complexity science.