por Are you interested in knowing what a genetic algorithm is and what it is for? If the answer is yes, we recommend that you continue reading this article, as we will tell you everything about this practice, how it works, how it is constituted and why you should learn it. So, to discover what a genetic algorithm is, be sure to read this article and go to the end, because we also offer you a great opportunity.
What is a genetic algorithm?
When science and computer science are integrated, genetic algorithm development appears in computer science. The genetic algorithm is a computer algorithm that allows you to solve questions related to optimization and search.
This is a concept that comes from the natural field of genetics and natural selection. That is, it takes as a reference the process in which species that can adapt to changes in the environment can survive and, in this way, reproduce and continue in the next generation.
Thus, the one with the greatest abilities among different individuals from different generations survives and, in this way, a problem is solved. All individuals will represent a search or solution for the program, as well as a string of characters, integers, floats or bits. The chain or string would play the role of the chromosome, as in genetics.
Elements of the genetic algorithm
To make the way in which the genetic algorithm works much more understandable, below we will list some of the most important elements of this practice in the computer sector. You will have to be clear about the meaning of the following elements:
Individual: one or several individuals are the elements that will be able to solve the program’s problems.
Population: In the case of the computer field, a population is a group of individuals.
Fitness function or adaptation function: It is a practice that aims to evaluate the behavior of individuals and check if they are part of the solution to problems.
crossover function: just as in the natural genetic process, when two elements or individuals are brought together, descendants are generated, that is, children, who are designed to provide better solutions than those provided by the parents to the problems of the program.
Genetic algorithm structure
Now that you know what a genetic algorithm is and what its possible elements are, which are reflected in the same process of natural genetics, you can understand how the genetic algorithm works based on its structure. This structure, as has happened in the previous sections, can be reflected in natural genetics.
Evaluation phase: Individuals will be evaluated based on the fitness or adaptation method or function.
Selection phase: Once evaluated, you will move on to the selection part.
Reproduction phase: It is the stage to cross individuals and obtain a «child» who can solve the problems.
Mutation phase: New changes or elements are introduced in the project development process to solve problems that may appear.
This way, you can record how you use the genetic algorithm and how you can use it to your advantage in your programming programs. machine learning or artificial intelligence.
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