International Journal of Computational Intelligence Research (IJCIR)

Volume 2, Number 4 (2006)


Hierarchical Two-Population Genetic Algorithm

Jarno Martikainen, Seppo J. Ovaska
Helsinki University of Technology, Institute of Intelligent Power Electronics, P. O. Box 3000, 02015 HUT, FINLAND


This paper proposes a new hierarchical twopopulation genetic algorithm (2PGA). The 2PGA scheme constitutes of two differently sized populations containing individuals of similar fitness or cost function values. The smaller population, the elite population, consists of the best individuals, whereas the larger population contains less fit individuals. These populations have different characteristics, such as size and mutation probability, based on the fitness of the candidate solutions in these populations. The performance of our 2PGA is compared to that of a single population genetic algorithm (SPGA). Because the 2PGA has multiple parameters, the significance and the effect of the parameters is also studied. Experimental results show that the 2PGA outperforms the SPGA reliably without increasing the amount of fitness function evaluations. Although genetic algorithms are used as a platform for the 2PGA scheme, the principles presented here are applicable also to other population based evolutionary optimization methods.

Genetic algorithms, multipopulation genetic algorithm, hierarchical populations, evolutionary algorithms, coevolution.