In genetic algorithms, 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 offsprings that are superior to their parents. Premature convergence can happen in case of loss of genetic variation (every individual in the population is identical, see 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.
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