Combining rules learnt using genetic algorithms for financial forecasting

Financial markets data present a challenging opportunity for the learning of complex patterns not otherwise discernable, and machine learning techniques like genetic algorithms have been noted to be advantageous in this regard. Independent trials of the genetic algorithm are known to explore different parts of the search space and produce solutions which potentially capture different patterns in the data. Additionally, learning in domains prone to noisy data can generate solutions which obtain performance gains by fitting to what essentially is noise in the data. The article investigates possible strategies for combining the rules obtained from independent GA trials with the objective of noise filtering or enhanced pattern detection for improving the overall learning accuracy.