ECE 4271

ECE 4271

Course information provided by the Courses of Study 2023-2024.

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

  •   Regular Academic Session.  Combined with: ECE 5271

  • 3 Credits GradeNoAud

  • 17932 ECE 4271   LEC 001

    • TR Bard Hall 140
    • Jan 22 - May 7, 2024
    • Delchamps, D

  • Instruction Mode: In Person