With his Biomorphs, evolutionary biologist Richard Dawkins used a genetic algorithm to evolve simple line drawings. Starting from a single pixel, Dawkins evolved a diversity of complex forms that resembled biological lifeforms found in nature --- insects, spiders, bats, frogs, birds. Dawkins' system was guided by user feedback. Rather than define an automatic fitness function in advance, Dawkins used his personal tastes, preferences, and intuition to determine the fitness of each individual, essentially working in tandem with the genetic algorithm to navigate the overwhelming large space of possible drawings. In this project, you will implement a system much like Dawkins', but for sound. You will design and implement a genetic algorithm to digitally synthesize sounds.

Sparse Search

Start a new search and evolve at least four generations. This time, use sparse fitness score distributions, meaning only two or three individuals are given high fitness scores of 1.0 and the rest are given low fitness scores of 0.0.

Question 2: How does the evolutionary trajectory of a sparse search differ from the trajectory of a search that is not sparse? Explain why.