Courtesy of MIT student, used with permission.
Genetic Algorithms (GA's) have been applied since the 1950's to simulate the evolution of a population. Soon enough, they became a widely known method to solve optimization problems in computer science and several genetic algorithmic systems were developed, such as Evolver. Genetic algorithms are not only suited for computer science; they may also be applied to artistic domains. For example, Ariza discusses an alternative application of GA's. Rather than moving towards a complex solution, Ariza's genetic algorithm system pulls from a trajectory of rhythmic populations that moves towards a simple solution. My work with Genetic Algorithms also focuses on creating interesting trends from an evolutionary process that moves towards a simple solution. I show that my system can be applied to granular synthesis to make compelling gestures...
Complete report (PDF)
Project Sample: Genetic Algorithms 1. (NOTE: this track starts at very low volume and emerges slowly.)
Project Sample: Genetic Algorithms 2.
Project Sample: Genetic Algorithms 3.
Code files (ZIP) (This ZIP contains 2 .py files.)