Title :
Selection of attributes for modeling Bach chorales by a genetic algorithm
Author_Institution :
Dept. of Comput. Sci., Waikato Univ., Hamilton, New Zealand
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 generated models. GA models outperformed the human models, improving compression by 10 percent
Keywords :
genetic algorithms; learning (artificial intelligence); music; performance evaluation; search problems; Bach chorale modeling; attribute selection; fitness evaluation criterion; genetic algorithm; human generated models; machine learning system; melodies; performance; pitch compression; search; test set; Computational modeling; Computer science; Context modeling; Genetic algorithms; Humans; Learning systems; Machine learning algorithms; Power system modeling; Predictive models; Testing;
Conference_Titel :
Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on
Conference_Location :
Dunedin
Print_ISBN :
0-8186-7174-2
DOI :
10.1109/ANNES.1995.499468
