Friday, February 04, 2005
GECCO 2005, learning classifier systems, and other genetics-based machine learning
This year I have the honor of being the chair of the GECCO track formerly known as learning classifier systems. For the last two years, I had the feeling that it was the moment to enlarge the scope of the track. Una-May agreed that this GECCO was the right one to give it a shoot.
Since the inception of learning classifier systems (LCS) by John Holland, learning paradigms driven by genetic algorithms (GA) have shown their competence on a broad spectrum of fields and applications. In a broader spectrum, genetics-based machine learning (GBML) systems have successfully tackled the creation of cognitive models, classification and prediction systems, and anticipatory behavior---to mention a few. Recently, GBML has been experiencing an important renaissance thanks to two key factors: (1) the new GA theoretical achievements have provided a better understanding of the underlying complex mechanisms used, and (2) the successful applications of such systems to real-world problems such as data mining.
I am very pleased to announce that instead of the 13 paper of GECCO 2004, we received 25 submissions to the track (We almost doubled the number of submission!). We have been able to maintain the core LCS group, and extend our reach to new interesting research in GBML. Una-May and I are very happy that the enlarge of the track scope was welcome by the research community. We also were able to competently deal with the uncertainty of the volume of submissions that a new track---such as the one I proposed to Una-May---would attract. Moreover, the track showed to be prepared to assume such volume increase.
Since the inception of learning classifier systems (LCS) by John Holland, learning paradigms driven by genetic algorithms (GA) have shown their competence on a broad spectrum of fields and applications. In a broader spectrum, genetics-based machine learning (GBML) systems have successfully tackled the creation of cognitive models, classification and prediction systems, and anticipatory behavior---to mention a few. Recently, GBML has been experiencing an important renaissance thanks to two key factors: (1) the new GA theoretical achievements have provided a better understanding of the underlying complex mechanisms used, and (2) the successful applications of such systems to real-world problems such as data mining.
I am very pleased to announce that instead of the 13 paper of GECCO 2004, we received 25 submissions to the track (We almost doubled the number of submission!). We have been able to maintain the core LCS group, and extend our reach to new interesting research in GBML. Una-May and I are very happy that the enlarge of the track scope was welcome by the research community. We also were able to competently deal with the uncertainty of the volume of submissions that a new track---such as the one I proposed to Una-May---would attract. Moreover, the track showed to be prepared to assume such volume increase.
