The premature convergence of a genetic algorithm arises when the genes of some high rated individuals quickly attain to dominate the population, constraining it to converge early and making it suboptimal. The premature convergence is generally due to the loss of diversity within the population. This loss can be caused by the selection pressure, the schemata distribution due to crossover operators, and a poor evolution parameters setting. This phenomenon occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents.
To avoid the premature convergence, in a genetic algorithm is imperative to preserve the population diversity during the evolution.
There are various strategies for preventing premature convergence as listed below:
- 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.