@inbook {
address = "Czech Technical University in Prague, FEE, Dep. of Comp. Sci. and Computers",
author = "KordÃk P.",
booktitle = "Hybrid Self-Organizing Modeling Systems",
chapter = "GAME - Hybrid Self-Organizing Modeling System based on GMDH",
editor = "Onwubolu, Godfrey C.",
pages = "290--340",
publisher = "Springer-Verlag, Berlin, Heidelberg",
series = "Studies in Computational Intelligence",
title = "GAME - Hybrid Self-Organizing Modeling System based on GMDH",
volume = "1",
year = "2009",
abstract = "In this chapter, an algorithm to construct hybrid self-organizing neural network is proposed. It combines niching evolutionary strategies, nature inspired and gradient based optimization algorithms (Quasi-Newton, Conjugate Gradient, GA, PSO, ACO, etc.) to evolve neural network with optimal topology adapted to a data set. The GAME algorithm is something in between the GMDH algorithm and the NEAT algorithm. It is capable to handle irrelevant inputs, short and noisy data samples, but also complex data such as "two intertwined spirals" problem.
The self-organization of the topology allows it to produce accurate models for various tasks (classification, prediction, regression, etc.). Bencharking with machine learning algorithms implemented in the Weka software showed that the accuracy of GAME models was superior for both regression and classification problems.
The most successful configuration of the GAME algorithm is not changing with problem character, natural evolution selects all important parameters of the algorithm. This is a significant step towards the automated data mining."
}