An Interactive Genetic Algorithm for Controller Parameter Optimization

  • Bogdan Filipič
  • Dani Juričić


Genetic algorithms are stochastic search algorithms inspired by biological phenomena of genetic recombination and natural selection. They simulate the evolution of string individuals encoding candidate solutions to a given problem. Genetic algorithms proved robust and efficient in finding near-optimal solutions in complex problem spaces. They are usually exploited as an optimization method, suitable for both continuous and discrete optimization tasks.

In this paper, genetic algorithms are investigated as an engineering optimization tool. The work focuses on tuning parameters in control system design. This domain has already been approached with genetic algorithms, but most of the experiments have been done using computer simulations of the devices to be controlled. We present the Interactive Genetic Algorithm (IGA) for controller parameter optimization. IGA carries out the evolution of parameter values in a traditional manner, but differs from a conventional genetic algorithm in that it allows interaction with real-world environment. The algorithm suggests the trials to be performed to explore the parameter space, and accepts results of the trials from the environment. The paper describes IGA and its application in tuning the parameters of a PID regulator operating on a laboratory device.


Genetic Algorithm Candidate Solution Settling Time Binary String Control System Design 
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-Verlag/Wien 1993

Authors and Affiliations

  • Bogdan Filipič
    • 1
  • Dani Juričić
    • 1
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia

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