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Abstract
In this thesis, two music accompaniment systems are presented. Evac (the evolutionary accompanist) is a system that engages in musical improvisation with the user. It uses a novel, implicitly interactive, genetic algorithm (GA), which allows the users actions to influence Evacs musical performance without the need for explicit rating of individuals. Evac runs in real time, allowing the user to experience the same kind of exploration that happens in real life improvisation scenarios with other musicians. EvolMusic is an accompaniment system involving human preference learning. It allows direct control from the user over the accompaniment by using machine learning techniques to learn a fitness function from the users preferences. EvolMusic records a piece of the users musical input, generates different accompaniments, lets the user vote for his or her favorite, adjusts the GAs fitness function, and then generates new accompaniments which can be further used to learn the users preferences.