HollandFest 09

I was unaware of this event, but the Illigal Blogging guys have brought to my attention the pointer to HollandFest 09. It's an event to celebrate the contributions professor John Holland has made to evolutionary computation, genetic algorithms, complex systems in general and emergence.

Professor David Goldberg has uploaded his presentation at HollandFest 09. It's about the further development of genetic algorithms from the late 1980s until nowadays, remembering some important lessons the lecturer has learnt along the time, giving emphasis to three ones he learnt from his former advisor. He is microblogging about it on Twitter.

So this year already begun so special to evolutionary computation. It's not only Darwin's 200th anniversary; nor 150 years since the publication of his seminal book; nor 45 years from the day two Germans students set up the experimentum crucis that would open one of the branches of evolutionary computation -- Ingo Rechenberg will celebrate his 75th anniversary in this year too!); nor the 20 years since the publication of Goldberg's book about genetic algorithms. But it's also on celebrating John Holland's 80th anniversary and all his contributions to the field he has helped to build.

EvoWeb Interviews John Koza

Check his interview here.

I have not been aware that John Koza was John Holland's student along the 1960s.

Interesting interview, despite my own disagreements on some points.

Koza summarizes his path towards genetic programming (GP), the choice for LISP as the main computer language of early GP implementations, the initial papers, the hostility from the GA community at that time (early 1990s), his practitioner style, his achievements, the 1000 nodes computer cluster at Stanford, and so on.

The interview is from 1998.

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P.S: There is someone that extremely disagrees upon Koza's statements.

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1984

No, this it not an allusion to the famous Orwellian work, but, let's say, the first time that someone posted on Usenet (now Google Groups) something that deals with genetic algorithms, see it here.

It is an announcement about the 1984 AAAI Conference, held in Texas, USA. Guess what? Who did speech on genetic algorithms? Let me give you a tip: JH... That's it! John Holland!

There were, I suppose, just two genetic algorithm presentations:

Michael L. Mauldin, Carnegie-Mellon University: Maintaining Diversity in Genetic Search

ABSTRACT
Genetic adaptive algorithms provide an efficient way to search large function spaces, and are increasingly being used in learning systems. One problem plaguing genetic learning algorithms is premature convergence, or convergence of the pool of active structures to a sub-optimal point in the space being searched. An improvement to the standard genetic adaptive algorithm is presented which guarantees diversity of the gene pool throughout the search. Maintaining genetic diversity is shown to improve off-line (or best) performance of these algorithms at the expense of poorer on-line (or average) performance, and to retard or prevent premature convergence..

And a machine learning tutorial session in which John Holland was involved in, see below:

9:00 a.m. to 10:30 a.m.

TUTORIAL AND PANEL: PARADIGMS FOR MACHINE LEARNING
Concert Hall in the Performing Arts

The session will be divided into two parts:

Center

Part 1. Tutorial.
A single presentation defining and outlining each
major approachto Machine Learning, and contrasting them with each other on the basis of objectives, techniques, limitations, and applications.

The role of the tutorial is to:

- Introduce each paradigm and the contrastive dimensions listed above.
- Present some meaningful comparative analysis.
- Raise potentially controversial issues to be addressed in the ensuing panel discussion.

Tutorial presenter: Jaime Carbonell

Part 2. Panel discussion.

Each Machine Learning paradigm will be represented by a panelist advocating that particular approach. The panelists are active researchers with considerable experience in ML in general and their approach in particular.

Discussion Leader: Patrick Winston

Panelists:

Tom Mitchell, Rutgers University (Analytical Generalization)
Ryzsard Michalski, University of Illinois (Empirical Induction)
John Holland (Genetic Algorithms)
Doug Lenat, Stanford University (Discovery Systems)
Jaime Carbonell, Carnegie-Mellon University (Learning by Analogy)

The panel discussion will center on addressing specific issues raised in the preceding tutorial (the panelists will be informed ahead of time of these issues). We are explicitly disallowing prepared statements by the panel -- we hope to have a real discussion focused around a few burning issues.

The picture above is Usenet circa 1985/1986.