Making IDEA-ARIMA Efficient in Dynamic Constrained Optimization Problems

  • Patryk Filipiak
  • Piotr Lipinski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)


A commonly used approach in Evolutionary Algorithms for Dynamic Constrained Optimization Problems forces re-evaluation of a population of individuals whenever the landscape changes. On the contrary, there are algorithms like IDEA-ARIMA that can effectively anticipate certain types of landscapes rather than react to changes which already happened and thus be one step ahead with the dynamic environment. However, the computational cost of IDEA-ARIMA and its memory consumption are barely acceptable in practical applications. This paper proposes a set of modifications aimed at making this algorithm an efficient and competitive tool by reducing the use of memory and proposing the new anticipation mechanism.


Benchmark Function Pareto Optimal Front Constraint Handling Anticipation Mechanism Future Landscape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

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

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