In evolutionary algorithms (EA), the term of premature convergence means that a population for an optimization problem converged too early, resulting in being suboptimal. In this context, the parental solutions, through the aid of genetic operators, are not able to generate offspring that are superior to, or outperform, their parents. Premature convergence is a common problem found in evolutionary algorithms in general and genetic algorithms in particular, as it leads to a loss, or convergence of, a large number of alleles, subsequently making it very difficult to search for a specific gene in which the alleles were present. An allele is considered lost if, in a population, a gene is present, where all individuals are sharing the same value for that particular gene. An allele is, as defined by De Jong, considered to be a converged allele, when 95% of a population share the same value for a certain gene (see also convergence). Strategies for preventing premature convergence Strategies to regain genetic variation can be: a mating strategy called incest prevention, uniform crossover, favored replacement of similar individuals (preselection or crowding), segmentation of individuals of similar fitness (fitness sharing), increasing population size. The genetic variation can also be regained by mutation though this process is highly random. One way to reduce the risk of premature convergence is to use structured populations instead of the commonly used panmictic ones, see below. Identification of the occurrence of premature convergence It is hard to determine when premature convergence has occurred, and it is equally hard to predict its presence in the future. One measure is to use the difference between the average and maximum fitness values, as used by Patnaik & Srinivas, to then vary the crossover and mutation probabilities. Population diversity is another measure which has been extensively used in studies to measure premature convergence. However, although it has been widely accepted that a decrease in the population diversity directly leads to premature convergence, there have been little studies done on the analysis of population diversity. In other words, by using the term population diversity, the argument for a study in preventing premature convergence lacks robustness, unless specified what their definition of population diversity is. Causes for premature convergence There are a number of presumed or hypothesized causes for the occurrence of premature convergence. Self-adaptive mutations Rechenberg introduced the idea of self-adaptation of mutation distributions in evolution strategies. According to Rechenberg, the