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Laga name comes from “Librería de Algoritmos Genéticos para Arquitectura”. The concept evolved but the name remains the same. The framework is designed for easy to use. Nevertheless, you will need some programming knowledge to used efficiently and overall understand the concept of genetic algorithms. I’m open to discussing how the structure work and how could be improved. The framework currently has 3 main topics. Genetic Algorithms, Graphics, and Interoperability.
###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 using Laga.GeneticAlgorithm; The structure of a generic GA works like this:
| step | operation | Laga |
|---|---|---|
| 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. |
| 2 | evaluate again. | Back to step 2 until stop. |