Note: The following provides a suggested course description,
objectives, and an outline. These may be modified pending discussion with
the Faculty Chairs, proposing faculty, and other curriculum
reviewers.
Course Description: Genetic algorithms are search
procedures based on the mechanics of natural genetics and natural
selection. They combine a Darwinian survival-of-the-fittest with
recombination and other genetic operators to form a search mechanism with
surprising breadth of application and efficiency. Genetic algorithms have
been applied to such diverse areas as computer-aided design,
communications network design, VLSI layout, immune system simulation, the
prisoner's dilemma problem, neural network adaptation and design, protein
folding and chemometrics, and horse-race handicapping. Genetic algorithms
are also receiving greater attention in machine learning, where they can
be used in classifier systems, a form of learning expert systems, or in
genetic programming, where the GA discovers better computing programs for
performing the task at hand. In this course, the theory and application of
genetic algorithms and other forms of evolutionary computation are
studied.
