Presentation of the University of Illinois LINK Alliance

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Presentation about the goals, motivations, and technologies of Learning In Networks of Knowledge (LINK) allience at the University of Illinois at Urbana-Champaign

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Presentation of the University of Illinois LINK Alliance

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.

Large Scale Data Mining using Genetics-Based Machine Learning

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We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.

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A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.

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