Description
This paper describes recent enhancements to GenJam, a genetic algorithm-based model of a novice jazz musician learning to improvise. After presenting an overview and update of the current interactive version of GenJam, we focus on efforts to augment its human fitness function with a neural network, in an attempt to ease the fitness bottleneck inherent in musical IGAs. Specifically, a cascade correlation technique was used with data taken from populations of musical ideas trained by human mentors interactively. We conclude with a discussion of why this approach failed, and we speculate on approaches that might work.
Date of creation, presentation, or exhibit
3-26-1996
Document Type
Conference Paper
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Recommended Citation
Biles, John; Anderson, Peter; and Loggi, Laura, "Neural network fitness functions for a musical IGA" (1996). Accessed from
https://repository.rit.edu/other/184
Campus
RIT – Main Campus
Comments
Proceedings of the International ICSC Symposium on Intelligent Industrial Automation (IIA'96) and Soft Computing (SOCO'96) 1996 Appears in: Proceedings of the International ICSC Symposium on Intelligent Industrial Automation (IIA'96) and Soft Computing (SOCO'96), March 26-28, Reading, U.K., ICSC Academic Press, ISBN 390-64-5401-0. Complete article also available at: http://www.it.rit.edu/~jab/SOCO96/SOCO.html ISBN: 390-64-5401-0Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.