Using Hybrid Algorithms Based on GMDH-Type Neural Networks for Solving Economic Problems

This survey deals with up-to-date results in the field of hybrid algorithms development of GMDH-type Neural Networks (GMDH-NN) and other methods of Artificial Intelligence (AI) which are successfully used for solving complex economic problems. Such hybrid algorithms are now only in its early stage of active research. General characteristics and main weaknesses of GMDH-NN are firstly presented. The paper further gives brief information on some AI paradigms such as Swarm Intelligence and Evolutionary Computation. Then known hybridization cases of GMDH-NN and certain methods of these paradigms (Genetic Algorithms, Differential Evolution, Particle Swarm Optimization, and Genetic Programming) are considered eliminating a number of the networks shortcomings. In the future it is worth to study the most promising ways of GMDH-NN hybridization with other methods of AI in order to increase their efficiency and extend applications.