A Comparative Analysis of Evolutionary Learning in Artificial Hydrocarbon Networks

Abstract

Artificial hydrocarbon networks (AHN) is a supervised learning model that is loosely inspired on the interactions of molecules in organic compounds. This method is able to model data in a hierarchical and robust way. However, the original training algorithm is very time-consuming. Recently, novel training algorithms have been applied, including evolutionary learning. Particularly, this training algorithm employed particle swarm optimization (PSO), as part of the procedure. In this paper, we present a benchmark of other meta-heuristic optimization algorithms implemented on the training method for AHN. In this study, PSO, harmony search algorithm, cuckoo search, grey wolf optimization and whale optimization algorithm, were tested. The experimental results were done using public data sets on regression and binary classification problems. From the results, we concluded that the best algorithm was cuckoo search optimization for regression problems, while there is no evidence that one of the algorithms performed better for binary classification problems.