Genetic algorithm

It is a paradigm of machine learning that generates the behavior patterns from the representation of evolution mechanisms. It is achieved through generation on the inside of the machine of the population of entities identified via genetics. People in the population will undergo a phase of mutation. It should be noted that growth will not be an assisted mechanism. Therefore, there is no evidence to support the theory that the purpose of evolution is to generate humans. So, the mechanisms of existence appear to come away to various Persons competing for services in the World. The following steps are used to obtain fitness using GA (Figure 4).
1. Consider populations p randomly.
2. Obtain the fitness of the population.
3. Repeat from Step 4 to 7 until convergence.
4. Choose any parent from the population individually.
5. Generates a new population through the crossover process.
6. Insert random genes in a new population to perform mutation.
7. Obtain fitness for newly generated populations.
The whole process is described as follows.
Suppose there is a target string, its goal is to produce target string starting from a random string with the same length. Its process for implementation is as follows.
1. The characters A-Z, a-z, 0-9, and other special symbols are considered as genes in GA.
2. The string generated by these characters is considered a chromosome.
The fitness score is the number of characters that are differed from characters in target string at a specific index of the string. Therefore, individuals having lower fitness value is given more preferences. According to the output of the algorithm, this approach having issues in the optimal solutions so that further improvement is needed to update the fitness score.