Genetic algorithm with population partitioning and space reduction for high dimensional problems

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.