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.