Lecture 8C: Elementary Cellular Automata, Stochastic Cellular Automata, Ising models, and Cellular Evolutionary Algorithms (2020-04-29)

In this lecture, we close out the semester's technical content with some final words about cellular automata (CA) and interacting particle systems (IPS). We keep the focus from the previous lecture on Elementary CA's (ECA's), reminding of the naming convention and a mention of some popular rules (such as rule 30, rule 110, rule 184, rule 232). We then pivot to stochastic/probabilistic CA's, with a focus on 1-D stochastic ECA and show how probabilistic rules do not only generate "fuzzy patterns." This allowed us to talk about Ising models and physical/hardware implementations of annealing machines for high-speed combinatorial optimization by metaheuristics. That closes some of the loop between the end of this class and earlier in the class. To fully close that loop, we close with an introduction to Cellular Evolutionary Algorithms (cEA's), which define genetic operators on spatially defined neighborhoods in a way clearly inspired by CA's (and these evolutionary algorithms can certainly be viewed as a kind of CA).

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/s/iwrwe59u5xt2y0v/IEE598-Lecture8C-Notes.pdf?dl=0