Xavier Llorà » Conferences

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

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 active interactive genetic algorithms using visual analytics

Xavier — Fri, 07 Jul 2006 19:54:53 +0000

by Xavier Llorà, Kumara Sastry , Francesc Alías, David E. Goldberg, and Michael Welge (2006).
Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 1417–1418, ACM press. Also as IlliGAL TR No 2006004. Link to the PDF.

Abstract
This paper build on active interactive genetic algorithms and introduces visual-analytic techniques to aggregate, summarize, and visualize the information generated during interactive evolutionary processes. Special visualizations of the user-provided partial ordering of solutions, the synthetic fitness surrogates induced, and the model of user preferences were prepared. The visual-analytic techniques proposed point out potential pitfalls, strengths, and possible improvements in a non-trivial case study where the hierarchical tournament selection scheme of an active interactive genetic algorithm is replaced by an equivalent incremental selection scheme. Visual analytics provided an intuitive reasoning environment that unveiled important properties that greatly affect the performance of active interactive genetic algorithms that could not have been easily reveled otherwise.

Fast rule matching for Learning Classifier Systems via vector instructions

Xavier — Fri, 07 Jul 2006 18:37:55 +0000

by Xavier Llorà and Kumara Sastry (2006, accepted).
Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 1513–1520, ACM press. Also as IlliGAL TR No 2006001. Link to the PDF.

Abstract
Over the last ten years XCS has become a de facto standard for Michigan-style learning classifier systems (LCS). Since the initial CS-1 work conceived by Holland, classifiers (rules) have widely used a ternary condition alphabet {0,1,#} for binary input problems. Most of the freely available implementations of this ternary alphabet in XCS rely on character-based encodings—easy to implement, not memory efficient, and expensive to compute. Profiling of freely available XCS implementations shows that most of their execution time is spent determining whether a rule is match or not, posing a serious thread to XCS scalability. In the last decade, multimedia and scientific applications have pushed CPU manufactures to include native support for vector instruction sets. This paper presents how to implement efficient condition encoding and fast rule matching strategies using vector instructions. The paper elaborates on Altivec (PowerPC G4, G5) and SSE2 (Intel P4/Xeon and AMD Opteron) instruction sets producing speedups of XCS matching process beyond ninety times. Moreover, such a vectorized matching code will allow to easily scale beyond tens of thousands of conditions in a reasonable time. The proposed fast matching scheme also fits in any other LCS other than XCS.

The compact classifier system: Motivation, analysis and first results

Xavier — Wed, 20 Jul 2005 22:13:47 +0000

by Xavier Llorà, Kumara Sastry, and David E. Goldberg (2006).
Proceedings of the Congress on Evolutionary Computation, 1, 596—603. Also as IlliGAL TR No 2005019. Link to the PDF.

Abstract
This paper presents an analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style classifier system. In order to be able to perform such an analysis we introduce a simple bare-bones Pittsburgh-style classifier systems—the compact classifier system (CCS)—based on estimation of distribution algorithms. Using a common rule encoding schemes of Pittsburgh-style classifier systems, CCS mantains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbated probability vectors. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules. The initial theoretical analysis and results also show that traditional encoding schemes used by Pittsburgh-style classifiers add an extra facet of diffiiculty. Such a bias plays a central role on the overall performance and scalability of CCS and other Pittsburgh-style systems using such encoding schemes.

Combating user fatigue in iGAs: Partial ordering, support vector machines, and synthetic fitness

Xavier — Tue, 19 Jul 2005 19:38:32 +0000

by Llorà, X., Sastry, K., Goldberg, D.E., Gupta, A., Lakshmi, L. (2005).
Published in the ACM Genetic and Evolutionary Computation Conference (GECCO 2005), ACM press, pp. 1363–1371. Also as IlliGAL TR No 2005009. Link to the PDF

Abstract:
One of the daunting challenges of interactive genetic algorithms (iGAs)—genetic algorithms in which fitness measure of a solution is provided by a human rather than by a fitness function, model, or computation—is user fatigue which leads to sub-optimal solutions. This paper proposes a method to combat user fatigue by augmenting user evaluations with a synthetic fitness function. The proposed method combines partial ordering concepts, notion of non-domination from multiobjective optimization, and support vector machines to synthesize a fitness model based on user evaluation. The proposed method is used in an iGA on a simple test problem and the results demonstrate that the method actively combats user fatigue by requiring 3–7 times less user evaluation when compared to a simple iGA.

Mining social networks in message boards

Xavier — Tue, 12 Apr 2005 19:49:54 +0000

by Matsumura, N., Goldberg, D.E., Llorà, X. (2005).
Published in the Symposium on Conversational Informatics for Supporting Social Intel ligence, The Society for the Study of Artificial Intelligence and the Simulation of Behavior Press, pp. 18–27. Also as IlliGAL TR No 2005001. Link to the PDF

Abstract:
In this paper, we first present an approach to extract social networks from message boards on the Internet. Then we show structural features of 3,000 social networks extracted from 3,000 message boards from 15 categories in Yahoo! Japan Message Boards to prove the relationships between the features and the categories. After we classify social networks into three types (interactive communication, distributed expertise communication and soapbox communication), we suggest an approach for mining social networks to identify the types of communication, the roles of individuals, and important ties, all of which can be used to redesign the means communication as well as understand the state of communication.