BlueJam Genetic Programming and Evolutionary Algorithms Capturing Creativity with Algorithmic Music Evolutionary Music Composition Machine Learning and Artificial Intelligence Goals and Achievements Music is a mode of creative expression present in culture. As an artform, music is appraised by listeners, but contention and conflict appear when the aesthetic critique attempts to define what makes music appreciable. Through modeling and analyzing the composition of music, we can hope to attain a better understanding of both listening and composing, illuminating the humanistic affinity with sound. Algorithms are used to describe computational processes. Modern synthesizers use algorithmic definitions to generate dynamic musical accompaniment; arpeggios, wave envelopes, drum patterns etc. Fixed algorithms are invariably deterministic, but this mantle of being “creative” can be tentatively ascribed to newer families of algorithms found in evolutionary computation. Previous research has experimented with mixed successes in the study of computer-generated and computer-assisted composition. Notable results have been achieved using constraint-based and grammatical systems, but evolutionary methods have made an appearance in programs which perform better than average. BlueJam attempts to further these achievements, adding some new concepts specific to music theory and a method of guiding the evolution through “Heuristic Trees”. Genetic Programming (GP) and Evolutionary Algorithms (EA) have produced human-competitive results when applied to problems of all kinds. Their approach to problem solving can be described as a search through the space of possible solutions. In 1992, Koza brought Tree-based GP to prominence, and it has been used widely ever since. The same paradigm is followed with Heuristic Trees, which uses the structure of the tree to specify an additional rhythm semantic. Modelling our approach on genetic behaviour, we can use crossover and mutation as two methods of altering the tree contents. During the program cycle, we move through several generations. In each generation, populations of musical trees are created and evaluated – the best are selected to create trees for the next generation. BlueJam can use an interactive interface to get the opinion of the user regarding current performance. After numerous generations, BlueJam will end up with a new set of heuristics that are attuned with the user. The traditional qualitative measure of artificial intelligence is the classic Turing test, in which one participant interacts with two agents in an attempt to classify which agent is the machine. Since there are tentative considerations regarding computational art, could there be a Turing-like test for Artificial Music Composers? Would this allow us to determine what is merely Generative and what is Creative? NoteCEbF(F#)… C … Eb … F … F#P(C)…P(Eb)..P(F)..… P(x) = Probability of the next note being x Fig 1. Musical Tree Structure (Genotype) The Heuristic tree defines the shape of the music, by locking specific properties like rhythm and pitch Leaf nodes get played (phenotype) Each level of the tree halves the rhythmic length of the nodes Context-free grammars can define note transitions Example for the C-Major Scale S := cC | dD |eE | f F| gG | aA | bB C := cD | fE | gF | gA | eaC … D := dA | dG | fgA …) E := … (multiple groupings can be defined) … Markov Models can assign probabilities to the grammar Example for Blues in C See More Visit BlueJam BlueJam is a framework for the evolution of music written in Java, interfaced with the open source MAX/MSP alternative “Pure-Data” (PD)