# Genetic Algorithms - Selection of Survivors

Tutorial on genetic algorithms
2020-11-20 01:06:35
# Genetic Algorithms - Selection of Survivors

The survivor selection policy determines which individuals should be deported and which should be retained in the next generation. It is crucial because it must ensure that the fittest individuals are not kicked out of the population, while preserving the diversity of the population.

Some GAs employ ** elitism **. Simply put, this means that the current fittest member of the population is always spread to the next generation. Therefore, under no circumstances can the fittest member of the current population be replaced.

The simplest policy is to expel random members of the population, but such an approach frequently poses convergence problems, therefore the following strategies are widely used.

## Selection based on age

In selection based on age'age, we do not have the notion of physical form. It is based on the principle that every individual is allowed in the population for a finite generation where he is allowed to reproduce, after that he is expelled from the population, regardless of his physical form.

For example, in the following example, age is the number of generations for which the individual was part of the population. The oldest members of the population ie P4 and P7 are expelled from the population and the age of the rest of the members is incremented by one.

## Fitness-based selection

In this fitness-based selection, children tend to replace less able individuals in the population. . The selection of the least fit individuals can be done using aVariation of one of the selection policies described above - tournament selection, selection commensurate with physical fitness, etc.

For example, in the following image, children replace the least fit individuals P1 and P10 in the population. Note that since P1 and P9 have the same fitness value, the decision to remove which individual from the population is arbitrary.