Random Forest RF is an ensemble learning approach that utilises a number of classifiers to contribute though voting to predicting the class label of any unlabelled instances. Parameters such as the size of the forest N and the number of features used at each split M , has significant impact on the performance of the RF especially on instances with very large number of attributes. In a previous work Genetic Algorithms has been used to dynamically optimize the size of RF. This study extends this

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... enetic algorithm approach to further enhance the accuracy of Random Forests by building the forest out of heterogeneous decision trees, heterogeneous here means trees with different M values. The approach is termed as Heterogeneous Genetic Algorithm based Random Forests (HGARF). As Random Forests generates a typical large number of decision trees with randomisation over the feature space when splitting at each node for all the trees, this has motivated the development of a genetic algorithm based optimisation. Typically, HGARF accepts as an input a forest −→ RF of N trees, the initial population is randomly generated from −→ RF as a number of smaller random forests − → rfi where each one has a number ni ≤ N of trees. This population of forests is then evolved through a number of generations using genetic algorithms. Our extensive experimental study has proved that Random Forests performance could be boosted using the genetic algorithm approach.