In this lecture, we transition from the Genetic Algorithm to alternative approaches in Evolutionary Computing, particularly Evolutionary Strategies and Evolutionary Programming. We start by highlighting that the Genetic Algorithm's typical mutation operators are a bit crude in that they use the same mutation policy for every decision variable. Motivated by this, we introduce Evolution Strategies, which are alternative approaches that allow for different mutation rates along different dimensions of the decision space. This lets us introduce the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is a popular global optimization approach that approximates the fitness objective with a multivariate normal sampling distribution. We then transition to more creative directions by showing how Evolutionary Programming, which removes crossover, allows for the evolution of Finite State Machines (FSM's) which are equivalent to computer programs that are automatically designed for a desired function. Next time, we will introduce Genetic Programming, which allows for evolving code directly using a more traditional GA (with crossover), and then we will transition to Immunocomputing, another genetically inspired approach to automated creativity.
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/cunoa5zqvx29uj3pm2n8j/IEE598-Lecture2A-2025-02-06-Evolutionary_Computing_from_Optimization_to_Programming-Notes.pdf?rlkey=tpqz2o3u36obp28xf4ipw7se2&dl=0
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