A Partheno-genetic Algorithm for Combinatorial Optimization

  • Maojun Li
  • Shaosheng Fan
  • An Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)


Genetic Algorithms (GA) Using ordinal strings for combinatorial optimization must use special crossover operators such as PMX, OX and CX, instead of general crossover operators. Considering the above deficiency of GA using ordinal strings, a Partheno-Genetic Algorithm (PGA) is proposed that uses ordinal strings and repeals crossover operators, while introduces some particular genetic operators such as gene exchange operators, which have the same function as crossover operators. The genetic operation of PGA is simpler and its initial population need not be varied and there is no “immature convergence” in PGA. The schema theorem of PGA was analyzed. Similarly with TGA, by genetic operators processing schemas, the individuals in the population continually move towards optimal individual in PGA, finally the optimal solution can be gained. The global convergence of PGA was studied. It was also proved that optimal maintaining operation is the key operation to make the algorithm global convergent.


Genetic Algorithm Global Convergence Crossover Operator Combinatorial Optimization Problem Genetic Operator 
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 2004

Authors and Affiliations

  • Maojun Li
    • 1
    • 2
  • Shaosheng Fan
    • 1
  • An Luo
    • 3
  1. 1.College of Electrical & Information EngineeringChangSha University of Science & TechnologyChangShaP.R. China
  2. 2.College of Information science & TechnologyCentral South UniversityChangShaP.R. China
  3. 3.College of Electrical & Information EngineeringHunan UniversityChangShaP.R. China

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