This document provides information about the NCSA/IlliGAL Gathering on Evolutionary Learning (NIGEL 2006) conference. It discusses how the conference originated from a previous 2003 gathering. It thanks the organizers and participants and provides details about the agenda, which includes presentations on topics like classifier systems and discussions around applications and techniques of evolutionary learning.
IFLA Global Vision & Focus - Nordic library meeting in CopenhagenMichel Steen-Hansen
IFLA Global Vision & Focus
Knud Schulz and Torbjörn Nilsson introductions
In the Nordic library organizations we believe that international cooperation can create a better world. That's one of the reasons why we meet, once a year, with all the other Nordic countries, discussing how we can strengthen cooperation between libraries for the benefit of people and society. (And I don't mention Trump at all)
This year we meet in Copenhagen and have a series of presentations and discussions, which you can see more about here www.biblioteksdebat.dk
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0Xavier Llorà
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 LearningXavier Llorà
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.
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...Xavier Llorà
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, and 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.
IFLA Global Vision & Focus - Nordic library meeting in CopenhagenMichel Steen-Hansen
IFLA Global Vision & Focus
Knud Schulz and Torbjörn Nilsson introductions
In the Nordic library organizations we believe that international cooperation can create a better world. That's one of the reasons why we meet, once a year, with all the other Nordic countries, discussing how we can strengthen cooperation between libraries for the benefit of people and society. (And I don't mention Trump at all)
This year we meet in Copenhagen and have a series of presentations and discussions, which you can see more about here www.biblioteksdebat.dk
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0Xavier Llorà
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 LearningXavier Llorà
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.
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...Xavier Llorà
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, and 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.
Linkage Learning for Pittsburgh LCS: Making Problems TractableXavier Llorà
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...Xavier Llorà
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.
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...Xavier Llorà
Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.
This presentation covers a brief overview of the current stage of the DISCUS project. General overview and introduction to some of the currently available tools
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Linkage Learning for Pittsburgh LCS: Making Problems TractableXavier Llorà
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...Xavier Llorà
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.
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...Xavier Llorà
Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.
This presentation covers a brief overview of the current stage of the DISCUS project. General overview and introduction to some of the currently available tools
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
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• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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https://arxiv.org/abs/2306.08302
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https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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1. Welcome to the
NCSA/IlliGAL Gathering on Evolutionary Learning
(NIGEL 2006)
Xavier Llorà
Automated Learning Group & Illinois Genetic Algorithms Lab
National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign
xllora@uiuc.edu
2. A Little Tale
LCS Gettogether in November 2003 at Ann Arbor.
•
John Holland, David Goldberg, Stewart Wilson, Stephanie
Forrest, Lashon Booker, Rick Riolo, Pier Luca Lanzi, Martin
Butz, and Xavier Llorà.
Discussed current trends and new directions.
Primordial soup of ideas and new challenges.
January 2006.
•
Friday’s coffee after the usual IlliGAL lunch meeting.
People discussing.
Talking to Dave Goldberg about GA related topic.
A question out of the blue:
“Have you thought on having another LCS gathering?”
Welcome to NIGEL 2006 Llorà, X. 2
3. An Unexpected Question
I was off balance. No answer.
•
Then the conversation move to other topics.
•
After the coffee I slowly recalled the 2003 Gettogether.
•
A retrospective look shows an irrefutable footprint in
•
the LCS/GBML research community.
One example:
• Stewart Wilson proposed “The frog problem”.
• The problem became the seed of a still blossoming amount of
papers on function approximation using LCS (see GECCO and
IWLCS).
Some issues discussed are still at bay waiting to sail
(encapsulation, the role of linguistics…) despite of interesting
attempts have been done.
Welcome to NIGEL 2006 Llorà, X. 3
4. Why Not?
I convinced myself about the usefulness of doing it
•
again.
I started contacting people with no much hope to bring
•
a small group together.
The feedback I made me think that may there was
•
hope.
So, off I went, and finally I was able to answer Dave’s
•
January question on February.
And the answer was: “Why Not?”.
•
At least, it will be fun for sure :D
•
Welcome to NIGEL 2006 Llorà, X. 4
5. Thank You!
Thanks to all of you for coming all the way here!
•
Thanks to Dave Goldberg for asking the right question,
•
for encouraging me to set NIGEL up, for supporting
NIGEL, and for his priceless advises and lessons.
Thanks to all IlliGAL labbies for taking care of precious
•
details.
Thanks to Michael Welge and the Automated Learning
•
Group at NCSA for their support and trust.
Thanks to NCSA for opening their doors and facilities to
•
us.
Welcome to NIGEL 2006 Llorà, X. 5
6. A Few Announcements & Logistics
NIGEL is going to be held in room 1030 at NCSA.
•
Coffee breaks will be held out side.
•
Internet access:
•
At IlliGAL (414 Transportation Building). Please see maps.
We have few cards to allow you inside.
Wireless access to the UIUCnet across campus (please visit
IlliGAL for instructions).
Moving around:
•
Maps on how to get to IlliGAL.
Campus map.
Welcome to NIGEL 2006 Llorà, X. 6
7. A Glance to the Agenda (I/III)
Tuesday, May 16 (open to public presentations and discussions)
•
8:30 am Welcome and presentation
9:00 am Public presentations:
Stewart W. Wilson: quot;Can We Do Captchas?”.
David E. Goldberg: quot;Searle, Intentionality, and the Future of Classifier
Systems”.
Dipankar Dasgupta: quot;Artificial Immune Systems in Anomaly Detection”.
Lashon Booker: quot;A Retrospective Look at Classifier System Research”.
10:30 am Break
10:45 am Public presentations:
Martin Butz: quot;XCS: Current Capabilities and Future Challenges”.
Alwyn Barry: quot;Towards a Formal Framework for Accuracy-based LCS”.
Xavier Llorà: quot;Linkage Learning for Pittsburgh Learning Classifier
Systems: Making Problems Tractable”.
Jorge Casillas: quot;Scalability in GBML, Accuracy-Based Michigan Fuzzy
LCS, and New Trends”.
12:15 pm Lunch
Welcome to NIGEL 2006 Llorà, X. 7
8. A Glance to the Agenda (II/III)
Tuesday, May 16 (open to public presentations and discussions)
•
1:50 pm Public presentations:
Ester Bernadó: quot;Learning Classifier Systems for Unbalanced Datasets”
Pier-Luca Lanzi: quot;Computed Prediction: so far, so good. Now what?”
Jaume Bacardit: quot;Pittsburgh Learning Classifier Systems for Protein
Structure Prediction: Scalability and Explanatory Power”
3:20 pm Break
3:30 pm Review of hot topics and round table
4:30 pm Break
4:40 pm Review of the goals of the Wednesday workshops
5:00 pm Break
7:00 pm Private reception (Party at Goldberg’s house).
Invitation only.
Welcome to NIGEL 2006 Llorà, X. 8
9. A Glance to the Agenda (III/III)
Tuesday, May 17 (closed workshops; topics may be modified)
•
8:30 am Why are we doing this?
• Moderator and brainstorm facilitator: David E. Goldberg
10:30 am Break
10:45 am Applications: Gaining visibility via something nobody
else can do?
• Moderator: Pier-Luca Lanzi
12:15 pm Lunch (provided)
1:50 pm Techniques: Where should we concentrate our efforts to
maximize the impact and visibility of LCS/GBML?
• Moderator: Martin Butz
3:20 pm Break
3:30 pm Review of hot topics and round table
3:30 pm Break
4:40 pm Thanks and good-bye!
Welcome to NIGEL 2006 Llorà, X. 9
10. Enough Talking. Let’s Get Some Action!
Welcome to
NCSA/IlliGAL Gathering on Evolutionary Learning
(NIGEL 2006)