Genetic algorithm with population partitioning and space reduction for high dimensional problems
Abdel-Rahman Hedar, Ahmed Fouad Ali · 2009
In this paper, we modify genetic algorithm (GA) with new strategies of population partitioning and space reduction for high dimensional problems. The proposed method is called GA with matrix-coding partitioning (GAMCP). In the GAMCP method, a population of chromosomes is coded in a one big matrix. This matrix is partitioned into several sub-matrices every generation, and GAMCP applies the genetic operations on the partitioned sub-matrices. Moreover, the gene matrix (GM) [5], [6] termination criteria are modified and applied in the GAMCP method in order to equip the search process with a self-check to judge how much exploration has been done and to maintain the population diversity. The computational experiments show the efficiency of the new elements proposed in the GAMCP method.