ECE 4271 - Evolutionary Processes. Evolutionary Algorithms, Evolutionary Games

Spring. 3 credits. Student option grading.

Prerequisite: ECE 3100 or a strong familiarity with discrete probability.

D. Delchamps.

Course addresses a collection of topics relevant to the modeling, analysis, simulation, and optimization of large complex multi-agent systems. Course 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.

Outcome 1: Develop an understanding of discrete-time Markov chains with countable state spaces.

Outcome 2: Learn about the historical development of various random-search techniques.

Outcome 3: Attain a fairly deep understanding of the theory of genetic algorithms.

Outcome 4: Attain a basic understanding of evolutionary game theory and its importance in modeling and analysis of modern large-scale systems.