A Niched-Penalty Approach for Constraint Handling in Genetic Algorithms

  • Kalyanmoy Deb
  • Samir Agrawal


Most applications of genetic algorithms (GAs) in handling constraints use a straightforward penalty function method. Such techniques involve penalty parameters which must be set right in order for GAs to work. Although many researchers use adaptive variation of penalty parameters and penalty functions, the general conclusion is that these variations are specific to a problem and cannot be generalized. In this paper, we propose a niched-penalty approach which does not require any penalty parameter. The penalty function creates a selective pressure towards the feasible region and a niching maintains diversity among feasible solutions for the genetic recombination operator to find new feasible solutions. The approach is only ap plicable to population-based approaches, thereby giving GAs (or other evolutionary algorithms) a niche in exploiting this penalty-parameter-less penalty approach.Simulation results on a number of constrained optimization problems suggest the efficacy of the proposed method.


Penalty Function Penalty Parameter Constraint Violation Constraint Handling Penalty Function Method 
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 1999

Authors and Affiliations

  • Kalyanmoy Deb
    • 1
  • Samir Agrawal
    • 1
  1. 1.Kanpur Genetic Algorithms Laboratory (KanGAL), Department of Mechanical EngineeringIndian Institute of TechnologyKanpurIndia

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