Infeasibility driven evolutionary algorithm with ARIMA-based prediction mechanism

Authors:
Patryk Filipiak;Krzysztof Michalak;Piotr Lipinski
Affiliations:
Institute of Computer Science, University of Wroclaw, Wroclaw, Poland;Institute of Business Informatics, Wroclaw University of Economics, Wroclaw, Poland;Institute of Computer Science, University of Wroclaw, Wroclaw, Poland
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

This paper proposes an improvement of evolutionary algorithms for dynamic objective functions with a prediction mechanism based on the Autoregressive Integrated Moving Average (ARIMA) model. It extends the Infeasibility Driven Evolutionary Algorithm (IDEA) that maintains a population of feasible and infeasible solutions in order to react on changing objectives faster. Combining IDEA with ARIMA leads to a more efficient evolutionary algorithm that reacts faster to the changing objectives which profits from using information coming from the prediction mechanism and remains one time instant ahead of the original algorithm. Preliminary experiments performed on popular benchmark problems confirm that the IDEA-ARIMA outperforms the original IDEA algorithm in many cases.