Infeasibility Driven Evolutionary Algorithm with Feed-Forward Prediction Strategy for Dynamic Constrained Optimization Problems

  • Patryk FilipiakEmail author
  • Piotr Lipinski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)


This paper proposes a modification of Infeasibility Driven Evolutionary Algorithm that applies the anticipation mechanism following Feed-forward Prediction Strategy. The presented approach allows reacting on environmental changes more rapidly by directing some individuals into the areas of most probable occurrences of future optima. Also a novel population segmentation on exploring, exploiting and anticipating fractions is introduced to assure a better diversification of individuals and thus improve the ability to track moving optima. The experiments performed on the popular benchmarks confirmed the significant improvement in Dynamic Constrained Optimization Problems when using the proposed approach.


Akaike Information Criterion ARIMA Model Infeasible Solution Main Loop Future Optimum 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Computational Intelligence Research Group, Institute of Computer ScienceUniversity of WroclawWroclawPoland

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