Xavier Llorà » Technical Reports

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 — 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.

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.

Observer-Invariant Histopathology using Genetics-Based Machine Learning

Xavier — Wed, 13 Dec 2006 20:58:47 +0000

by Xavier Llorà, Anusha Priya, and Rohit Bhargava (2006).
To appear in the Special Issue on Learning Classifier Systems of the Natural Computing Journal. Also as IlliGAL TR No. 2006027. Link to the PDF.

Abstract
Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men, and it is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopy of stained tissue is now able to provide very large data sets describing the chemical properties of the cells forming the prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), we seek to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learn tissue type classification—a key step on the diagnosis process. As we will show, genetics-based machine learning (GBML) can be used to approach such a problem. However, there is an urge for efficient and scalable implementations that enable to process such very large data sets. This paper proposes and validates and efficient GBML technique—NAX—based on an incremental genetics-based rule learner that exploits massive parallelisms—via the message passing interface (MPI)—and efficient rule-matching using hardware-implemented operations. Results show the competence of NAX solving the prostate tissue type prediction and how such and efficient implementation makes it a very powerful tool for biomedical image processing.

Delineating Topic and Discussant Transitions in Online Collaborative Environments

Xavier — Wed, 13 Dec 2006 18:26:10 +0000

by Noriko Imafuji Yasui, Xavier Llorà, and David E. Goldberg (2006).
Illinois Technical Report No. 2006025. Link to the PDF.

Abstract
In this paper, we propose some methodologies for delineating topic and discussant transitions in online collaborative environments, more precisely, focus group discussions for product conceptualization. First, we propose KEE (Key Elements Extraction) algorithm, an algorithm for simultaneously finding key terms and key persons in a discussion. Based on KEE algorithm, we propose approaches for analyzing two important factors of discussions: discussion dynamics and emerging social networks. Examining our approaches using actual network-based discussion data generated by real focus groups in a marketing environment, we report interesting results that demonstrate how our approaches could effectively discover knowledge in the discussions.

Adaptable Extraction of Key Elements from Weblogs

Xavier — Wed, 13 Dec 2006 15:27:24 +0000

by Xavier Llorà, Noriko Imafuji Yasui, David E. Goldberg (2006).
Illinois Technical Report No. 2006024. Link to the PDF.

Abstract
This paper proposes AKEE (Adaptable Key Elements Extraction) algorithm for web-log (blog, for short) mining. AKEE enables us to identify significant information in various blog components (e.g., a blog post, a series of blog posts, a set of series of blog posts, etc.).

Human-Centered Analysis and Visualization Tools for the Blogosphere

Xavier — Wed, 29 Nov 2006 20:31:08 +0000

by Xavier Llorà, Noriko Imafuji Yasui, Michael Welge, David E. Goldberg (in press, 2007).
To apper in the Proceedings of the Digital Humanities 2007 Conference.Also as IlliGAL TR No. 2006023. Link to the PDF.

Abstract
Blogging has become a new and disruptive communication medium. Blogs have changed the way people and organizations express, interact, and—quite unforeseen—exercise influence. The digital nature of the blog media provides access to an always-expanding corpus of information. It would take more than a lifetime to read all the available blogs necessary to answer questions such as what were the more relevant plots suggested or what key concepts were managed by bloggers in their ideas. However, human-centered analysis and visualization techniques may help users navigate such enormous corpus. This paper presents human-centered analysis and visualization techniques for supporting innovation and creativity can help to identify relevant post portions and to visualize concept relations in the blogosphere.

Evolving emotional prosody

Xavier — Sun, 17 Sep 2006 15:57:30 +0000

by Cecilia Ovesdotter Alm and Xavier Llorà (2006).
Proceedings of the Ninth International Conference on Spoken Language Processing (INTERSPEECH 2006), paper 1741. Also as IlliGAL TR No 2006018. Link to the PDF.

Abstract
Emotion is expressed by prosodic cues, and this study uses the active interactive Genetic Algorithm to search a wide space for sad and angry parameters of intensity, F0, and duration in perceptual resynthesis experiments with users. This method avoids large recorded databases and is flexible for exploring prosodic emotion parameters. Solutions from multiple runs are analyzed graphically and statistically. Average results indicate parameter evolution by emotion, and appear best forsad speech. Solutions are quite successfully classified by CART, with duration as main predictor.

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.