3rd December 2017
Initial prototype delivered 4th December, 2017
Classroom delivery is ready for Semester 1, 2018.
One of a series of Neural Network experiments in various languages, to understand the core types of Machine Learning that are becoming prevalent in industry and academia. This example leverages the processing language due to its reduced complexity and is based on the artificial intelligence concepts of Neuroevolution.
An Evolutionary Simulator developed in Processing using Genetic Algorithms.
This program generates 1000 creatures. The creatures are very simple and very very stupid. However, they get a lot smarter, very quickly, usually within a thousand generation.
Creatures have nodes (blobs of mass) and muscles (simple springs between nodes). Nodes have variable mass, speed and friction and Muscles have variable strength and speed. Most creatures in generation 1 will fall over. That’s just the way the Universe works.
Creatures also have 3 measures that define their fitness:
Over hundreds of generations, the elegant triangle usually rises to the top, but in some cases quadrupeds and wheels develop! That’s evolution!
You can adjust the following:
This demonstration will be used in Biology 1st year with approximately 2,000 students to demonstrate the “how and the why” of digital literacies scaffolded into their learnings.