ECE 4271

ECE 4271

Course information provided by the Courses of Study 2019-2020.

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.

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

Enrollment Information
Syllabi: 1 available
  •   Regular Academic Session. 

  • 3 Credits Stdnt Opt

  • 17645ECE 4271  LEC 001

  • Instruction Mode: Hybrid - Online & In Person