Standard Quadratic Optimization problems are an important family of problems with a large number of applications in finance, decision science, graphs algorithms, etc.
For these nonconvex problems, we are interested in finding global optima; however, exact global optimization algorithms hardly scale with problem size. The goal of the thesis is to experiment with classical global optimization strategies that may exploit efficient, tailored local optimization techniques in order to obtain a consistent computational speed up.

Memetic Algorithms for Standard Quadratic Programming Problems