IlliGAL Blogging

The trip to Mexico was interesting for several reasons. The CONCEV 2005 conference was a pool of ideas. I was sitting at Francisco Herrera talk on memetic algorithms and I could not stop thinking how fun was we both were there, and neither of us were giving a talk about genetics-based machine learning. But, that was not the reason for feeling uncomfortable sitting in a very comfortable chair in the auditorium; Francisco’s talk was sharp and inspiring. Suddenly, I just realized that there was something else buzzing in my mind: how easy we may be trying to reinvent the wheel.

In another words, sometimes we realize that after inverting our efforts on a particular endeavor, someone had already gone through the same problem, pain, and thought process. Sometimes trying to face the problem straight without looking around first is a good guarantee for reinventing the wheel. In a previous post, Dave Goldberg has coined the term WORN (write once, read never) for such a phenomenon of postmodern times. Thinking about it, it is hard not to be trap in it with the explosion of available information and the time required to successfully dive in it looking for the clue we need.

Among other reasons, the WORN wheel issue is the tip of the iceberg of why I am currently revisiting four genetics-based machine learning papers. Since I am a Pittsburgh style guy, let me suggest first:

Neri F. and Giordana A. (1995). "A Distributed Genetic Algorithm for Concept Learning", Proceedngs of the International Conference on Genetic Algorithms (Pittsburgh, PA), Morgan Kaufmann, pp. 436-443.


Konstam, A. (1994) “N-group classification using genetic algorithms”, Proceedings of the 1994 ACM symposium on Applied computing (Phoenix, AZ), ACM Press, pp. 212-216.

The other pillow papers I would like to suggest are Stewart Wilson’s ones. These two papers are greatly responsible for changing how people approached the so-called Michigan style classifier systems, setting the basis of the current efforts in the field.

Wilson, S.W. (1991) "The Animat Path to AI". In From Animals to Animats: Proceedings of The First International Conference on Simulation of Adaptive Behavior, J.-A. Meyer and S.W. Wilson, eds., Cambridge, MA: The MIT Press/Bradford Books, pp. 15-21.


Wilson, S.W. (1995), "Classifier fitness based on accuracy", Evolutionary Computation, 3(2), 149-175.