Imitation of Bee Reproduction as a Crossover Operator in Genetic Algorithms

  • Ali Karcı
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3157)


There are lots of methods inpired by the natural observations (i.e. fuzzy logic, artificial neural networks, genetic algorithms, simulated annealing algorithms, etc.) This paper proposes a novel crossover operator type inspired by the sexual intercourses of honey bees. The method selects a specific chromosome in present population as queen bee. While the selected queen bee is one parent of crossover, all the remaining chromosomes have the chance to be next parent for crossover in each generation once. For this purposes, we defined three honey bee crossover methods: In the first method, the chromosome with the best fitness score is queen honey bee and it is a fixed parent for crossover in the current generation. The second method handles the chromosome with the worst fitness score. Finally, queen bee is changed sequentially in each generation.


Genetic Algorithm Sexual Intercourse Simulated Annealing Algorithm Fitness Score Uniform Crossover 
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.


  1. 1.
    Goldberg, D.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company Inc., Massachusetts (1989)Google Scholar
  2. 2.
    Karcõ, A., Arslan, A.: Bidirectional evolutinary heuristic for the minimum vertex-cover problem. Journal of Computers and Electrical Engineerings 29, 111–120 (2003)CrossRefGoogle Scholar
  3. 3.
    Karcõ, A., Arslan, A.: Uniform Population in Genetic algorithms. İ.Ü. Journal of Electrical & Electronics 2(2), 495–504 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ali Karcı
    • 1
  1. 1.Faculty of Engineering, Department of Computer EngineeringFirat UniversityElazιğTurkey

Personalised recommendations