Evolutionary Intelligence explores the possible interactions among human performers and genetic algorithms. Inspired by nature, genetic algorithms explore large search spaces using techniques gleaned from the biological process of evolution. These techniques are characterized by iterative refinement of a working solution using genetic operators such as crossover and mutation.
This piece unfolds in various ways as we hear what the algorithm develops in response to a human performer. The goal is not to evolve to a finished piece, but to enjoy the sonic experience of what the genetic algorithm creates as it moves toward a goal using natural selection. During the performance, this goal may change, and the population may expand or crash as it would when an organism migrates to a small island.