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\citeA{jordanous10} reported a computational jazz improvisation system using genetic algorithms. Genetic algorithms, and evolutionary computing more generally, could encourage computational serendipity. We examine Jordanous's system (later given the name {\em GAmprovising} \cite{jordanous:12}) as a case study for evolutionary computing in the context of our model of computational serendipity: to what extent does GAmprovising model serendipity?
GAmprovising uses genetic algorithms to evolve a population of Improvisors. Each Improvisor is able to randomly generate music based on various parameters such as range of notes to be used, preferred notes to be used, rhythmic implications around note lengths and other musical parameters (see \cite{jordanous10}. These parameters are what defines the Improvisor at any point in evolution. After a cycle of evolution, each Improvisor is evaluated via a fitness function based on Ritchie's criteria \cite{ritchie07} of how creative the Improvisor
is, based on is. Ritchie's criteria use user-supplied ratings of how novel and how appropriate the music produced by the Improvisor
is, to calculate 18 criteria that collectively evaluate how creative a system is. The most successful Improvisors
(as deemed by the fitness function) are used to seed a new generation of Improvisors, through crossover and mutation operations.
The GAmprovising system can be said to have a \textbf{prepared mind} through its background knowledge of what musical knowledge to embed in the Improvisors and the evolutionary abilities to evolve Improvisors. A \textbf{serendipity trigger} comes
partly from the
combination of the mutation and crossover operations employed in the genetic
algorithm algorithm, and
partly from the user input feeding into the fitness function to evaluate produced music. A \textbf{bridge}, from the genetic algorithm operations and user input, to the result is built by the creation of new Improvisors. The \textbf{results} are the various musical improvisations produced by the fittest Improvisors (as well as, perhaps, the parameters that have been considered fittest).
The
\textbf{chance} likelihood of serendipitous evolution is greatly enhanced by the use of mutation
as well as and crossover operations within the genetic algorithm, to increase the diversity of search space covered by the system during evolution.
However the \textbf{chance} of any particular Improvisor being discovered is low, given the massive dimensions of the search space. Interesting developments in evolution would be guided by \textbf{curiosity}
as driven by through the
particular human user
input upon listening to the evolved Improvisors' music. identifying Improvisors as interesting at that time. \textbf{Sagacity} is determined by how likely the user is to appreciate the same Improvisor's music (or similar music) over time, as tastes of the user may change. The \textbf{value} of the results are maximised through employing a fitness function.
Evolutionary systems such as GAmprovising necessarily operate in a \textbf{dynamic world} which is evolving
continously continuously and may also be affected by changes in user tastes as they evaluate musical output from Improvisors. The \textbf{multiple contexts} arise from the possibility of having multiple users evaluate the musical output (though this is as yet not implemented formally) or through the user changing their preferences over time. \textbf{Multiple tasks} are carried out by the system including evolution of Improvisors, generation of music by individual Improvisors, capturing of user ratings of a sample of the Improvisors' output, and fitness calculations. \textbf{Multiple influences} are captured through the various combinations of parameters that could be set and the potential range of values for each parameter.
% \paragraph{{[}To add: HR.{]}}
