A herd of heuristic algorithms is compared using a portfolio optimization. Previously “A comparison of some heuristic optimization methods” used two simple and tiny portfolio optimization problems to compare a number of optimization functions in the R language. This post expands upon that by using a portfolio optimization problem that is of a realistic size … Continue reading...
Genetic algorithm is a search heuristic. GAs can generate a vast number of possible model solutions and use these to evolve towards an approximation of the best solution of the model. Hereby it mimics evolution in nature. GA generates a population, the individuals in this population (often called chromosomes) have Read more »
A simple portfolio optimization problem is used to look at several R functions that use randomness in various ways to do optimization. Orientation Some optimization problems are really hard. In these cases sometimes the best approach is to use randomness to get an approximate answer. Once you decide to go down this route, you need … Continue reading...
The book is by Manfred Gilli, Dietmar Maringer and Enrico Schumann. I haven’t actually seen the book, so my judgement of it is mainly by the cover (and knowing the first two authors). The parts of the book closest to my heart are optimization, particularly portfolio optimization, and particularly particularly portfolio optimization via heuristic algorithms. … Continue reading...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
