Genetic algorithms for the structural optimisation of learned polynomial expressions

摘要

This paper presents a hybrid genetic algorithm approach to construct optimal polynomial expressions to characterise a function described by a set of data points. The algorithm learns structurally optimal polynomial expressions (polynomial expressions where both the architecture and the error function have been minimised over a dataset), through the use of specialised mutation and crossover operators. The algorithm also optimises the learning process by using an efficient, fast data clustering algorithm to reduce the training pattern search space. Experimental results are compared with results obtained from a neural network. These results indicate that this genetic algorithm technique is substantially faster than the neural network, and produces comparable accuracy.