METAHEURISTIC HYBRIDIZATION: PRINCIPLES AND METHODS
METAHEURISTIC HYBRIDIZATION: PRINCIPLES AND METHODS

Summary/Abstract: Lately, more and more investors are fans of easy gains, and the easiest way is to invest in stocks, even if the risks they have to take are very high, but these are not the last problem. The real problem for investors is to find the right price formula and, for that, they have to build forecast models, to make some assumptions for default variables, which are difficult to understand. To refine the prediction process, researchers have developed hybrid models for forecasting. A well-known class of these techniques is in the area of metaheuristics. The purpose of this paper is to present the hybridization process and hybrid algorithms. Also, another goal is to reveal some possibilities of metaheuristics, such as hybridization of algorithms.

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