An intro about evolutionary algorithms

DNA - Evolutionary Algorithms
DNA - Evolutionary Algorithms

Evolutionary algorithms (EA) are a type of optimization algorithm that mimic the process of natural selection and evolution.

In nature, organisms adapt to their environment through the process of natural selection, which favors the organisms that are best suited for their environment. Over time, these organisms reproduce and their offspring inherit their favorable traits, resulting in the adaptation of the species to its environment. Evolutionary algorithms work in a similar way. They start with a population of solutions (organisms) and through a process of selection and reproduction, the algorithms evolve a population of solutions that are better suited to the problem at hand.

There are a number of different evolutionary algorithms, but all of them share three basic components: selection, reproduction, and mutation.


In evolutionary algorithms, selection is the process of choosing the best solutions from the population and discarding the rest. The best solutions are usually chosen based on some measure of fitness, which is a measure of how well the solution solves the problem.


Reproduction is the process of creating new solutions by combining the best solutions from the population. This can be done in a number of ways, but the most common approach is to select two solutions at random and combine their features to create a new solution. Mutation: Mutation is the process of changing a solution in some way. This can be done randomly or by choosing the best solutions and mutating them.


Mutation is used to introduce new solutions into the population and to prevent the population from becoming too static.

What it is used for?

Fields that may apply evolutionary programming include computer science, artificial intelligence, operations research, management science, information systems, business analytics, and bioinformatics.

In AI, evolutionary algorithms are often used to improve the performance of machine learning algorithms. For example, a genetic algorithm may be used to optimize a neural network for a particular task.

Wanna go through the rabbit hole?

Rabitt hole - A Rabbit

If you liked this intro, here are some tips we recommend in case one wants to learn more about evolutionary algorithms. Start by reading more about the different variants of evolutionary algorithms and understand the basics of how they work. After that, you can start trying to apply evolutionary algorithms to different problems that you're familiar with, in order to get a better understanding of how they work in practice. Finally, you can read more about the theoretical foundations of evolutionary algorithms to learn more about why they work the way they do.

Here are a couple of references we recommend:

  1. A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery” by A.A. Freitas
  2. Genetic Programming: On the Programming of Computers by Means of Natural Selection” by John R. Koza.