Operators used in GA

When the initial generation is generated, the GA evolves the generation using the operators, discussed as follows.

Operator for Selection

In this kind of operator, the individuals have a preference with better fitness score and enable them to pass their gene to the successive generation of the individual.

Operator for Crossover

This reflects a generation of breeding among persons. Randomly choose two individuals using selection operators and crossover operators. After chosen, genes are at the crossover sites exchanged, so building a completely new offspring or individual. Figure 2 shows the crossover operator.

Operator for Mutation

To use this tool, introduce random genes into offspring to retain the genetic heterogeneity to prevent excessive divergences. Figure 3 shows the mutation process.