Hybrid Model of Genetic Algorithm and Cultural Algorithms for Optimization Problem

  • Fang Gao
  • Hongwei Liu
  • Qiang Zhao
  • Gang Cui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


To solve constrained optimization problems, we propose to integrate genetic algorithm (GA) and cultural algorithms (CA) to develop a hybrid model (HMGCA). In this model, GA’s selection and crossover operations are used in CA’s population space. A direct comparison-proportional method is employed in GA’s selections to keep a certain proportion of infeasible but better (with higher fitness) individuals, which is beneficial to the optimization. Elitist preservation strategy is also used to enhance the global convergence. GA’s mutation is replaced by CA based mutation operation which can attract individuals to move to the semi-feasible and feasible region of the optimization problem to improve search direction in GA. Thus it is possible to enhance search ability and to reduce computational cost. A simulation example shows the effectiveness of the proposed approach.


Genetic Algorithm Hybrid Model Constrain Optimization Problem Real Code Genetic Algorithm Belief Space 
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
  • Hongwei Liu
    • 1
  • Qiang Zhao
    • 2
  • Gang Cui
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.School of Traffic Transportation EngineeringNortheast Forestry UniversityHarbinP.R. China

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