A simple genetic algorithm for music generation by means of algorithmic information theory

— Recent large scale experiments have shown that the Normalized Information Distance, an algorithmic information measure, is among the best similarity metrics for melody classification. This paper proposes the use of this distance as a fitness function which may be used by genetic algorithms to automatically generate music in a given pre-defined style. The minimization of this distance of the generated music to a set of musical guides makes it possible to obtain computer-generated music which recalls the style of a certain human author. The recombination operator plays an important role in this problem and thus several variations are tested to fine tune the genetic algorithm for this application. The superiority of the relative pitch envelope over other music parameters, such as the lengths of the notes, brought us to develop a simplified algorithm that nevertheless obtains interesting results.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Manuel Alfonseca, Manuel Cebrián, Alfonso Ortega
• Keywords: Algorithmic Information Measure, Artificial Intelligence, CEC 2007, Genetic Algorithm, Normalized Information Distance