Genetic Algorithms Survivor Selection - Genetic Algorithms

What is Genetic Algorithms Survivor Selection?

The Survivor Selection Policy defines which persons are to be hit out and which are to be kept in the next generation. It is critical as it must ensure that the fitter persons are not hit out of the population, while at the same time variety should be maintained in the population.

Some GAs employs Elitism. In humble terms, it means the current fittest member of the population is always propagated to the next generation. So, under no circumstance can the fittest member of the current population be replaced.

The coolest policy is to kick random members out of the population, but such a method regularly has convergence issues, therefore the following strategies are widely used.

Age Based Selection

In Age-Based Selection, we don’t have an idea of fitness. It is created on the principle that each distinct is allowed in the population for a finite generation where it is allowed to replicate, after that, it is hit out of the population no matter how good its fitness is.

For example, in the resulting instance, the age is the number of generations for which the distinct has been in the population. The oldest members of the population i.e. P4 and P7 are kicked out of the population and the ages of the rest of the members are incremented by one.
age_based_selection

Fitness Based Selection

In this fitness based selection, the children are likely to substitute the least fit persons in the population. The selection of the least fit persons may be done using a difference of any of the selection policies described before – tournament selection, fitness proportionate selection, etc.

For instance, in the resulting image, the children replace the least fit individuals P1 and P10 of the population. It is to be noted that since P1 and P9 have the same fitness value, the decision to remove which individual from the population is arbitrary.

fitness_based_selection

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