Set Oriented Numerics
In finance, GARCH models are widely used, for example to identify trend, risk and variation in the markets. Nevertless, the choice of the optimal parameters is a hard task. Because of this, in most of cases methods fail and solutions are not good enough.The optimization algorithm presented in this work is a memetic algorithm, which uses two techniques, differential evolution coupled with mathematical programing techniques. The first one is used to find an initial value, and mathematical programing is use to guarantee convergence.We present numerical results on a benchmark related to time series and GARCH models.
- Pareto Explorer for the Local Exploration of Many Objective Optimization Problems
- The Gradient Subspace Approximation for Scalar Optimization
- Archivers for the Set of Approximate Solutions on Multi-objective Optimization
- Hypervolume Newton method as a Local Searcher for Indicator based Evolutionary Algorithm
- Benjamin Perea