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Evolutionary Processes, Evolutionary Algorithms, Evolutionary Games

Credits 3
Tags Systems Math Probability
Key Topics Markov Chains, Genetic Algorithms, Evolutionary Game Theroy
Prerequisites ECE 3100 or a strong familiarity with discrete probability.
Course Tags Last offered: SP25

Class Overview

This course addresses a collection of topics relevant to the modeling, analysis, simulation, and optimization of large complex multi-agent systems. It also 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.

Professor: Dr. David Delchamps

Semester(s): Spring

Difficulty: 2.5/5

Rating: 4/5

Assignments: Weekly problem sets.

Exams: One midterm and one final exam.

Pros

  • Overall workload is light.
  • Homeworks and exams graded fairly 'lightly'.
  • Little linear algebra or complicated calculus concepts needed for prerequisites.
  • Interesting concepts.

Cons

  • Very theoretical, math heavy course. Probably not too much application for most ECE majors.

Tips for Success

  • Make sure to go to lecture! As usual, Delchamps provides course notes but the lecture content is more focused, and key points are highlighted more.