In order to optimize for the traveling salesman problem, there are certain points that must be taken care of. This inclu

[answerhappygod]

In order to optimize for the traveling salesman problem, there
are certain points that must be taken care of. This includes
initialization of 6 chromosomes in the initial population and
choosing a parent selection mechanism among BT, FPS, RBS. There is
a crossover probability between 1-1 and mutation probability of
0-0.5 . Survival selection is done using (mu + lambda) with
different methods for parent selection namely BT, FPS and RBS
respectively but all come out to the same result after 30
simulations over 100 iterations each with 100 individuals per
simulation (with the exception) for FPS where there is a lower
probability of 40 to 50 individuals that may be selected over the
others. The random numbers throughout operation must also be
recorded. There are many things that can be done when working on
this problem but the point of this article is to show how to obtain
a solution to one of the most famous problems in operation
research, and with such simple and frequently used methods
(especially in biotechnology). There are many implementations of
the traveling salesman problem by different researchers and also
many articles that contain many new approaches to solving this
problem. But, we will use some of the most notable or popular
methods mentioned and obtain the solution using them. Our work
should be compared with those who have used an approach similar to
ours because it is based on their works that we have been able to
come up with the results. While working on this problem, the ideas
about how to obtain reasonable results to fit the criteria of a
solution must be discussed in order to obtain acceptable results.
We hope this article will be of help to others in solving some
problems that are also originated from the traveling salesman
problem and will also give them extra knowledge or application of
known models.
INITIALIZATION
The first thing we must do is initialize 6 chromosomes in the
initial population. We can call these genes and contain information
for each gene in several ways such as BT, FPS, RBS, etc... The
random number generator used by each biologist should be recorded
but the method that was used by this example was (GAMBETTA).The
following are examples of the initialization:
Initialization of genes:
BT, FPS, RBS and GAMBETTA were the algorithms used to initialize
genes. For BT, when selecting individuals with similar products
like GAMBETTA in order to obtain a large population with such
possibility. For FPS and RBS there are no restrictions for the
individual values for each gene other than it must be greater than
or equal to one. Lastly for GAMBETTA, which is a random number
generation technique that can be used instead of (GAMBETTA). It
allows us to have more control over our initial population,
especially using BT where we can have better results if we choose
individuals with similar products.
The following table shows the individuals with similar products for
the BT, FPS, RBS and GAMBETTA initializations.
I chose to initialize 6 individuals with a starting value of 25,000
and our results were: (Tables 1 and 2)
Table 1 Initialization Results For BT (in billions)
Table 2 Initialization Results For FPS (in billions)
Since we have already obtained a result of 100 billion in our
previous example, we expect that if we start with this much again
which would be 10 billion individuals and also assuming that each
individual is chosen with probability 0.5 then the solution will be
found by this algorithm by 1 billion iterations. The next step is
to find out the parent selection mechanism.