Discussing the article: "Population optimization algorithms: Binary Genetic Algorithm (BGA). Part II"

[MetaQuotes]

Check out the new article: Population optimization algorithms: Binary Genetic Algorithm (BGA). Part II.

In this article, we will look at the binary genetic algorithm (BGA), which models the natural processes that occur in the genetic material of living things in nature.

The development of the genetic binary algorithm was inspired by several factors and ideas. The main ones:

• Natural selection and principles of evolution: BGA is based on the principles of natural selection and evolution proposed by Charles Darwin. The idea is that there is a diversity of solutions in a population, and those that are better adapted to the environment are more likely to survive and pass on their characteristics to the next generation.
• Genetics and heredity: BGA also uses genetics concepts such as gene, chromosome and heredity. Solutions in BGA are represented as binary strings, where individual groups of bits represent specific genes, and the gene in turn represents the parameter being optimized. Genetic operators, such as crossover and mutation, are applied to binary strings to create new generations of solutions.

Overall, the development of BGA was the result of a combination of ideas from the fields of evolutionary algorithms, genetics and optimization. It was created to solve optimization problems using the principles of natural selection and genetics, and its development continues to this day, a huge number of GA options have been created, as well as the widespread use of ideas and approaches in genetic algorithms as part of hybrid algorithms, including very complex ones.

The Binary Genetic Algorithm (BGA) uses a binary representation of data. This means that each individual (solution) is represented as a string of bits (0 and 1). Genetic operators, such as crossover and mutation, are applied to bit strings to create new generations of solutions.

Author: Andrey Dik

[fxsaber]

Thanks for the Article! Added the algorithms to the general list.