Article thumbnail

Selection of attributes for modelling Bach chorales by a genetic algorithm

By Mark A. Hall

Abstract

A genetic algorithm selected combinations of attributes for a machine learning system. The algorithm used 90 Bach chorale melodies to train models and randomly selected sets of 10 chorales for evaluation. Compression of pitch was used as the fitness evaluation criterion. The best models were used to compress a different test set of chorales and their performance compared to human generate models. G.A. models outperformed the human models, improving compression by 10 percent

Topics: computer science, genetic algorithm
Publisher: 'Institute of Electrical and Electronics Engineers (IEEE)'
Year: 1995
DOI identifier: 10.1109/ANNES.1995.499468
OAI identifier: oai:researchcommons.waikato.ac.nz:10289/1511

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles

Citations

  1. (1975). Adaption in Natural and Artificial Systems.
  2. (1986). Foretelling the Future by Adaptive Modeling”. doi
  3. (1989). Genetic Algorithms in Search, Optimisation, and Machine Learning.
  4. (1990). Prediction and entropy in music. doi
  5. (1949). The Mathematical Theory of Communication. doi