This Chapter is about the documentation related to Genetic Algorithms Classes. The classes to design and build the GAs. Read the following pages to understand the laga architecture. The library is designed for easy to use. Nevertheless, you will need some programming knowledge to used efficiently and overall understand the concept of genetic algorithms.
This section provides all the necessary tools to design and create Genetic Algorithms. The structure is very simple to use.
Call the reference:
The structure of a generic GA works like this:
|1||Creates a random population||Use the class GenrChromosome.cs and or GenrPopulation.cs. But it will depend on your objective.|
|2||Evaluation||Is up to you, it depends on your problem.|
|3||select the individuals with the highest evaluation||Use the class NaturalSelection.cs|
|4||crossovers the selected individuals to produce inheritance||Use the class Crossover.cs|
|5||mutate the inheritance||Use the class Mutation.cs|
|6||replace the original population||Use the class Replacement.cs or develop your own method.|
Revised documentation: 2019/06/20