Integration of Genetic Algorithm and Cultural Algorithms for Constrained Optimization

  • Fang Gao
  • Gang Cui
  • Hongwei Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


In this paper, we propose to integrate real coded genetic algorithm (GA) and cultural algorithms (CA) to develop a more efficient algorithm: cultural genetic algorithm (CGA). In this approach, GA’s selection and crossover operations are used in CA’s population space. GA’s mutation is replaced by CA based mutation operation which can attract individuals to move to the semifeasible and feasible region of the optimization problem to avoid the ‘eyeless’ searching in GA. Thus it is possible to enhance search ability and to reduce computational cost. This approach is applied to solve constrained optimization problems. An example is presented to demonstrate the effectiveness of the proposed approach.


Genetic Algorithm Constrain Optimization Problem Crossover Operation Search Ability Real Code Genetic Algorithm 
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 Berlin Heidelberg 2006

Authors and Affiliations

  • Fang Gao
    • 1
  • Gang Cui
    • 1
  • Hongwei Liu
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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