ECE 5271

Evolutionary Processes, Evolutionary Algorithms, Evolutionary Games

Course Description

Course information provided by the 2025-2026 Catalog.

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.

Prerequisites ECE 3100 or a strong familiarity with discrete probability.

Last 4 Terms Offered 2025SP, 2024SP, 2022SP

Outcomes

• 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