Microcontroller Implementation of a Multi Objective Genetic Algorithm for Real-Time Intelligent Control

  • Martin Dendaluce
  • Juan José Valera
  • Vicente Gómez-Garay
  • Eloy Irigoyen
  • Ekaitz Larzabal
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)


This paper presents an approach to merge three elements that are usually not thought to be combined in one application: evolutionary computing running on reasonably priced microcontrollers (μC) for real-time fast control systems. A Multi Objective Genetic Algorithm (MOGA) is implemented on a 180MHz μC.A fourth element, a Neural Network (NN) for supporting the evaluation function by predicting the response of the controlled system, is also implemented. Computational performance and the influence of a variety of factors are discussed. The results open a whole new spectrum of applications with great potential to benefit from multivariable and multiobjective intelligent control methods in which the hybridization of different soft-computing techniques could be present. The main contribution of this paper is to prove that advanced soft-computing techniques are a feasible solution to be implemented on reasonably priced μC -based embedded platforms.


Soft-Computing Intelligent Control Multi Objective Genetic Algorithm NSGA-II Artificial Neural Network Microcontroller 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Martin Dendaluce
    • 2
  • Juan José Valera
    • 2
  • Vicente Gómez-Garay
    • 2
  • Eloy Irigoyen
    • 1
    • 2
  • Ekaitz Larzabal
    • 2
  1. 1.Computational Intelligence Group, Department of Systems Engineering and Automatic ControlUniversity of the Basque Country (UPV/EHU), ETSIBilbaoSpain
  2. 2.Intelligent Control Research Group, Department of Systems Engineering and Automatic ControlUniversity of the Basque Country (UPV/EHU), ETSIBilbaoSpain

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