Jan and Deb, extended the well-know NSGA-II to deal with many-objective optimization problem, using a reference point approach, with non-dominated sorting mechanism. The newly developed algorithm is simply called: NSGA-III. The main reference paper is available to download, here. In this post, we are going to share with you, the MATLAB implementation of NSGA-III, as an open source project. The ...

Downloads The download link of this project follows. Portfolio Optimization using Classic Methods and Intelligent Methods (PSO, ICA, NSGA-II, and SPEA2) in MATLAB Download Citing This Work If you wish, you can cite this content as follows. Cite as: Mostapha Kalami Heris, Classic and Intelligent Portfolio Optimization in MATLAB (URL: https://yarpiz.com/391/ypap112-portfolio-optimization), Yarpiz, 2015.

Feature selection is one of common preprocessing tasks, which is performed to reduce the number of inputs of intelligent algorithms and models. This helps us to simplify the models, reduce the computation cost of model training, and enhance the generalization abilities of the model and prevention of over-training. For more information on feature selection concepts and methods, you can refer ...

Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a multi-objective genetic algorithm, proposed by Deb et al., in 2002. It is an extension and improvement of NSGA, which is proposed earlier by Srinivas and Deb, in 1995. In the structure of NSGA-II, in addition to genetic operators, crossover and mutation, two specialized multi-objective operators and mechanisms are defined and utilized: Non-dominated ...