This study involves diploid genetic algorithms in which a diploid representation of individuals is used. This type of representation allows characteristics that may not be visible in the current population to be preserved in the structure of the individuals and then be expressed in a later generation. Thus it prevents traits that may be useful from being lost. It also helps add diversity to the genetic pool of the populations. In conformance with the diploid representation of individuals, a reproductive scheme which models the meiotic cell division for gamete formation in diploid organisms in nature is employed. A domination strategy is applied for mapping an individual's genotype onto its phenotype. The domination factor of each allele at each location is determined by way of a statistical scan of the population in the previous generation. Classical operators such as cross-over and mutation are also used in the new reproductive routine. The next generation of individuals are chosen via a fitness proportional method from among the parents and the offspring combined. To prevent early convergence and the population overtake of certain individuals over generations, an age counter is added. The effectiveness of this algorithm is shown by comparing it with the simple genetic algorithm using various test functions.