Evolutionary computation, such as genetic algorithm and genetic programming, has found many applications in science and engineering. One of the recent tests on algorithms is the SAT problem, which refers to the satisfiability testing a set of variables of a given Boolean expression to make it true. It is an example of combinatorial optimization problem that one uses often in physics, such as the problem of protein folding, in engineering, such as logistics. One can also apply the techniques in time series analysis in econophysics.

Course type:

UROP1000 UROP1100 UROP2100 UROP3100 UROP4100

Applicant's Roles:

Applicant is expected to know how to do numerical simulation and has some background in statistical physics.

Applicant's Learning Objectives:

Data analysis and analytical calculation using some mathematics in statistics will be
conducted.
1. To learn the basics of genetic algorithm
2. To apply improved genetic algorithms to the various optimization problems