Presentation Zen


Lately I have been thinking about how to effectively communicate ideas over presentations. This has turned to be a key element when trying to convey the benefits of jumping on the Meandre wagon. Presentation Zen is a very interesting resource for methods, techniques, and example on how to convey and communicate ideas. I am working […]

Lately I have been thinking about how to effectively communicate ideas over presentations. This has turned to be a key element when trying to convey the benefits of jumping on the Meandre wagon. Presentation Zen is a very interesting resource for methods, techniques, and example on how to convey and communicate ideas. I am working on revamping some of my Meandre presentations trying to be able to get the points across easily.

Prof. Chua interviewed by “El País”

Prof. León Chua visited Barcelona to preside over the examining committee of one of our former members and current collaborators, Giovanni Pazienza. On this visit, he had time to share his main discovery with us in the talk: “Memristor: 37 years later”.
Through an unusual presentation, overhead transparencies, amusing jokes, technological explanations hidden in […]

Prof. León Chua visited Barcelona to preside over the examining committee of one of our former members and current collaborators, Giovanni Pazienza. On this visit, he had time to share his main discovery with us in the talk: “Memristor: 37 years later”.
Through an unusual presentation, overhead transparencies, amusing jokes, technological explanations hidden in […]

Lecture 02: Machine Learning for Data Mining

This lecture provides a brief overview of the area of Machine Learning and discusses its relation to Data Mining. Course 80916 on Data Mining and Text Mining, Master of Science in Engineering Computing Systems, Facoltà di Ingegneria dell’Informazione, Politecnico di Milano.

This lecture provides a brief overview of the area of Machine Learning and discusses its relation to Data Mining. Course 80916 on Data Mining and Text Mining, Master of Science in Engineering Computing Systems, Facoltà di Ingegneria dell’Informazione, Politecnico di Milano.

Lecture 01: Data Mining

This lecture provides an overview of the areas of Knowledge Discovery in Databases (KDD) and Data Mining. Course 80916 on Data Mining and Text Mining, Master of Science in Engineering Computing Systems, Facoltà di Ingegneria dell’Informazione, Politecnico di Milano.

This lecture provides an overview of the areas of Knowledge Discovery in Databases (KDD) and Data Mining. Course 80916 on Data Mining and Text Mining, Master of Science in Engineering Computing Systems, Facoltà di Ingegneria dell’Informazione, Politecnico di Milano.

Lecture 00: Course Introduction

Short introduction to the course 80916 on Data Mining and Text Mining, Master of Science in Engineering Computing Systems, Facoltà di Ingegneria dell’Informazione, Politecnico di Milano.

Short introduction to the course 80916 on Data Mining and Text Mining, Master of Science in Engineering Computing Systems, Facoltà di Ingegneria dell’Informazione, Politecnico di Milano.

Wolfram|Alpha is going life in two months

Some time ago I was told about the project Wolfram|Alpha by the creator of Mathematica and the author of a new kind of science (NKS), Stephen Wolfram. This project aims at going beyond the typical process of search engines by proposing a system that computes the answers of user questions. That is, instead of […]

Some time ago I was told about the project Wolfram|Alpha by the creator of Mathematica and the author of a new kind of science (NKS), Stephen Wolfram. This project aims at going beyond the typical process of search engines by proposing a system that computes the answers of user questions. That is, instead of going to the data an retrieve information by the syntactic similarity with the user question, the new architecture will try to figure it out the answer, which may not be explicitly written in the web documents, by processing the data. For this purpose, Wolfram proposes to use Mathematica and the NKS to explicitly implement methods and models, as algorithms, and explicitly curate all data so that it is immediately computable. In addition, there must be the help of human experts to formalize each domain.

Therefore, a new approach, very different to that of natural language processing, that promises to make knowledge computable. Fortunately, I will need to wait only two monts to answer all the questions that arose after reading the Wolfram blog.

Evolutionary design of Evolutionary Algorithms

Abstract  Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult
task. This is why we have to find other manners to construct algorithms that perform very well on some proble…

Abstract  Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult
task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility
(which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for
solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here.
The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular
problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical
experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known
benchmarking problems.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-009-9081-6
  • Authors
    • Laura Dioşan, Babeş-Bolyai University Department of Computer Science, Faculty of Mathematics and Computer Science Kogalniceanu 1 Cluj-Napoca 400084 Romania
    • Mihai Oltean, Babeş-Bolyai University Department of Computer Science, Faculty of Mathematics and Computer Science Kogalniceanu 1 Cluj-Napoca 400084 Romania

Medical applications as a growth area for genetic and evolutionary computing

Within the last week we’ve been notified of several new citations to GPEM articles on medical/pharmaceutical applications, which is consistent with my impression that this is a particularly promising growth area for the field.

Our special issue on “Medical Applications of Genetic and Evolutionary Computation” (guest editors Stephen L. Smith and Stefano Cagnoni) was published in December of 2007, and we have published related work both before and after that special issue — for example we published “Use of genetic programming to diagnose venous thromboembolism in the emergency department” by Milo Engoren and Jeffrey A. Kline in March, 2008, and two relevant articles in September, 2008 (“Genetic programming for medical classification: a program simplification approach” by Mengjie Zhang and Phillip Wong, and “Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks” by Julio J. Valdes, Alan J. Barton, and Arsalan S. Haqqani). Also upcoming and now in Online First: “Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis” by Stephan M. Winkler, Michael Affenzeller and Stefan Wagner.
I think that there’s  a lot of potential here both for new applications and for GPEM to bring more of the ongoing work to the broader research community. I would encourage researchers who work in this area to contact me about possibilities.

Within the last week we’ve been notified of several new citations to GPEM articles on medical/pharmaceutical applications, which is consistent with my impression that this is a particularly promising growth area for the field.

Our special issue on “Medical Applications of Genetic and Evolutionary Computation” (guest editors Stephen L. Smith and Stefano Cagnoni) was published in December of 2007, and we have published related work both before and after that special issue — for example we published “Use of genetic programming to diagnose venous thromboembolism in the emergency department” by Milo Engoren and Jeffrey A. Kline in March, 2008, and two relevant articles in September, 2008 (“Genetic programming for medical classification: a program simplification approach” by Mengjie Zhang and Phillip Wong, and “Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks” by Julio J. Valdes, Alan J. Barton, and Arsalan S. Haqqani). Also upcoming and now in Online First: “Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis” by Stephan M. Winkler, Michael Affenzeller and Stefan Wagner.
I think that there’s  a lot of potential here both for new applications and for GPEM to bring more of the ongoing work to the broader research community. I would encourage researchers who work in this area to contact me about possibilities.