Tuesday, September 18, 2007
Thursday, September 13, 2007
Evolutionary Computation Portrait
The image above is a very nice one that I made by myself for a small tutorial upon the History Of Evolutionary Computation.
It's a very nice picture!
TOP ROW - From left to right:
MIDDLE ROW - From left to right:
Kenneth De Jong
Hans Joachim Bremermann
BOTTOM ROW - From left to right:
Walter Bradford Cannon
John Burdon Sanderson Haldane
Alan Mathison Turing
I would like to thank Professor Hans-Paul Schwefel for his nice answers upon some questions I asked.
Sunday, September 02, 2007
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
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:
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
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.