In this lecture, we review Elementary Cellular Automata (ECA's) and some classic rules and how to interpret them. We then transition to a brief discussion of hardware-based cellular automata for things like the Ising model, which allows for hardware-based annealing machines that perform simulated annealing (SA) optimization very fast. That lets us transition to discussing stochastic CA's (SCA) and then conclude with an introduction to cellular evolutionary algorithms (cEA's) which put different selective pressures on the population based on the distribution of the individuals in a space (where CA-like neighborhoods define the subpopulations on which a GA is to act, as if the GA was the CA update rule). That concludes the class for Spring 2022.
Whiteboard notes for this lecture can be found at: [ Ссылка ]
This lecture was recorded by Theodore Pavlic as part of IEE/CSE 598 (Bio-Inspired AI and Optimization) at Arizona State University.
