ECE 4271

Evolutionary Processes, Evolutionary Algorithms, Evolutionary Games

Course Description

Course information provided by the 2024-2025 Catalog. Courses of Study 2024-2025 is scheduled to publish mid-June.

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

Last 4 Terms Offered 2025SP, 2024SP, 2022SP, 2020SP

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.

When Offered Spring.

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