Identification of AIDS Disease Severity Using Genetic Algorithm - Computational Intelligence Techniques for Comparative Genomics - page 101

Fig. 4 The “ Genetic
Algorithm ” process flow chart

C. Crossover operation
The crossover operation takes two individuals as input expressions and selects a
random point and interactions the sub-expression at the rear point. While
implementing the crossover operation, there may be chance of having errors
such as a crossover may produce individuals in which an attribute may be
involved more than once and the offspring may be the same as that of the parent
expression with a probability of 1. To overcome these errors, we used the
below-mentioned techniques while performing crossover operation.
If two individual parents have been selected for crossover,

the crossover

operator is considered as follows:

1. Select one individual expression randomly among the attributes and
exchange the corresponding term between them if the two individuals have
same attributes
2. Select one attribute randomly from individual parent expressions, respec-
tively, and exchange the corresponding term between the two individuals if
there are not same attribute between the individual parents.
D. Mutation operation
Mutation operation plays a vital role in the global optimization process. This
operator maintains the diversity of gene in the population and guarantees that
the search is done in the absolute solution space.
Considering an individual parent “ p ” with a rule length “ n, ” the mutation

operator is de ned as follows: