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Chromosome represents the solution
to the problem and each chromosome contain a string of genes 4. The genetic
algorithm implement either a set of binary alphabet {0, 1}, numerical symbol or
characters depending on the problem solving and collection of the chromosomes
is referred as a population 3. During the optimisation process, these
chromosomes will undergo a fitness function measurement to check for its
suitability or capability of the solution generated by the genetic algorithm
for the problem 3.

1.1. Constrain handling method

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1.2. Crossover

Crossover in GA is an essential
operation as the chromosome’s characteristics are exchanged between the
individuals of population. The principle of crossover a process of partial
probability in which multiple chromosome are joined to create a new generation.
The parent’s strings combine and perform a random cut between two chromosomes
as shown in Figure 2.

of crossover can be calculate by using the formula:

While Ncrossover=number of crossover,
Pcrossover=probability of crossover, Ncrossover=number of

1.3. Mutation

Mutation occurred when obtaining
new solution, a parent chromosome is selected and combine with another parent chromosome
to undergo mutation to create a new child chromosome. The main purpose of mutation
is to control and maintain the diversity within the population and avoid premature
convergence. Avoid premature convergence means that if the chromosomes created
in the initial population are in same value such as 1 at a particular
position, then all the future offspring will also have same value 1 in this
position 5. Hence, mutation will help to mutate the gene randomly into other
value with a low probability. Every value in the chromosome will be check for
its mutation possibility by generating a random number between 0 and 1 and if
this number is lower or equal to the given mutation probability, then the value
will be change 5. Besides, there are several mutation method as shown in Figure below.

Probability of the mutation can be
calculated by using formula:

While Nmutation=number of
mutation, Pmutation=probability of mutation, Npopulation=number
of population

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