ECE 4271

Evolutionary Processes, Evolutionary Algorithms, Evolutionary Games

Course Description

Course information provided by the Courses of Study 2021-2022.

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.

When Offered Spring.

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

• Develop an understanding of discrete-time Markov chains with countable state spaces.
• Learn about the historical development of various random-search techniques.
• Attain a fairly deep understanding of the theory of genetic algorithms.
• Attain a basic understanding of evolutionary game theory and its importance in modeling and analysis of modern large-scale systems.

View Enrollment Information