CCE Theses and Dissertations
Date of Award
2011
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Information Systems (DCIS)
Department
Graduate School of Computer and Information Sciences
Advisor
Maxine S Cohen
Committee Member
Sumitra Mukherjee
Committee Member
Maria Niederberger
Keywords
bottleneck, crowdsourcing, mTurk, music
Abstract
When genetic algorithms (GA) are used to produce music, the results are limited by a fitness bottleneck problem. To create effective music, the GA needs to be thoroughly trained by humans, but this takes extensive time and effort. Applying online collective intelligence or "crowdsourcing" to train a musical GA is one approach to solve the fitness bottleneck problem. The hypothesis was that when music was created by a GA trained by a crowdsourced group and music was created by a GA trained by a small group, the crowdsourced music would be more effective and musically sound. When a group of reviewers and composers evaluated the music, the crowdsourced songs scored slightly higher overall than the songs from the small-group songs, but with the small number of evaluators, the difference was not statistically significant.
NSUWorks Citation
Jessica Faith Keup. 2011. Computer Music Composition using Crowdsourcing and Genetic Algorithms. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, Graduate School of Computer and Information Sciences. (197)
https://nsuworks.nova.edu/gscis_etd/197.