Genetic algorithms and their large number of applications

 Genetic algorithms are a tool based on the natural selection process to obtain solutions to search and optimization problems.

The main idea in these algorithms is to mimic the process of natural selection, such as inheritance, mutation, crossing, selection, etc. In this way, individuals will be the possible solutions to the problem at hand. Furthermore, these algorithms do not require a large amount of data for their development.

How does this algorithm proceed?

We start from an initial set of solutions, which will be the initial population. Those that we consider to be the best solutions, evaluated according to the so-called fitness function, will be selected to transmit the information to the next generation in different ways, so that this new generation will inherit the information from the previous generation. The generations follow one another in such a way that each time they will obtain better individuals, that is, better solutions to the problem.


A schematic form of the algorithm:

1.            Generation of the initial population of solutions.

2.            Evaluation of the population generated

3.            Selection of the best individuals

4.            Building a new generation

5.            Repeat steps 2, 3, and 4 until completion condition is reached

The solutions that we will find will be fast and efficient. The initial population of the algorithm cannot be too large or too small. As a general rule, we initialize it randomly, thus allowing all kinds of diversity in our population and guaranteeing optimal solutions. In general, all generations will have the same number of individuals.

Fitness function: what is it and what does it measure?

The fitness function will help us evaluate all individuals. With it, we will be able to assign a value to each individual in the generation in such a way that, if one solution obtains a higher value than another, it will represent that it is a better solution, or a solution closer to the optimal solution that is sought. Thus, we will be able to know which individuals are better within a generation. We need a suitable fitness function for each problem.

How do we generate the successors of the next generation?

Before generating the successors of the next generation, we must select the individuals that we will use as parents and, sometimes, we will select which children will pass to the next generation. Sometimes we will simply clone from parent to child, to ensure that we preserve the original genetics.

It is important that the selection favors the best according to their assessment of fitness function, and that it allows diversity. It usually also has a random component.

Each individual will have a probability of being selected, proportional to their assessment, with respect to the assessments of the total population. The best individuals will have a higher probability and, therefore, will be selected more frequently.

What applications do genetic algorithms have?

There are a large number of problems that can be solved by genetic algorithms. Some of these problems are:

Case 1: Route optimization. Applied in route optimization, it allows you to find the shortest or fastest route between cities or areas in a very short time. It is also very useful in Smart cities, to try to reduce CO2 emissions.

Case 2: Task optimization. When it comes to getting tasks done in the shortest time possible, optimizing them is essential. With genetic algorithms, the calculation is also fast and efficient.

Case 3: Automated management of industrial equipment. It is possible to make a calculation in real time to optimize an automated process of industrial equipment.

Case 4: Learning robot behavior. Robot learning is possible with respect to a cost function. With these algorithms, learning is done faster and more efficiently.

Case 5: Financial sector systems. An interesting application in finance is that these algorithms allow discovering investment rules that indicate when to enter and exit a market to obtain the maximum benefits. On the other hand, in Splitwise methods, the method of sharing expenses between different users can be optimized.

Case 6: Finding errors in programs . It allows you to detect errors in the programs of the developers, which helps to save time and money in their implementation.

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