Evolutionary Processes, Evolutionary Algorithms, Evolutionary Games

Class Overview

This course addresses a collection of topics relevant to the modeling, analysis, simulation, and optimization of large complex multi-agent systems. It also provides a standalone introduction to discrete-time Markov chains; covers the Metropolis algorithm and its generalizations; gives an introduction to the theory of genetic algorithms; and provides an introduction to evolutionary game theory, including the ESS concept, replicator dynamics, and dynamic probabilistic approaches.

Prerequisites: ECE 3100 or a strong familiarity with discrete probability.
Key Topics: Markov Chains, Genetic Algorithms, Evolutionary Game Theroy