Evolución Diferencial (DE) is a powerful, population-based algoritmo de optimización that is particularly effective for solving complex, nonlinear, and multi-dimensional problems. It belongs to the family of evolutionary algorithms, which are inspired by the process of natural selection.
The core idea behind Differential Evolution is to evolve a population of candidate solutions over a series of iterations, known as generations. Each individual in the population represents a potential solution to the de optimización. DE operates by combining existing solutions to generate new candidate solutions, which are then evaluated based on a predefined objective function.
El algoritmo sigue estos pasos principales:
- Inicialización: A population of candidate solutions is randomly generated within the defined search space.
- Mutación: For each candidate solution, a new solution is created by adding a scaled difference between two randomly selected members of the population to a third member.
- Cruce: The mutated solution is combined with the original candidate solution to create a trial solution. This is typically done using a crossover probability que determina cuánto de la nueva solución se mezcla con la original.
- Selección: The trial solution is compared to the original candidate solution. If the trial solution has a better fitness valor, reemplaza la solución original en la población.
Este ciclo de mutación, cruce y selección continúa hasta que se cumple un criterio de parada, como un número máximo de generaciones o un umbral de error aceptable.
Differential Evolution is known for its simplicity and effectiveness, making it a popular choice in various fields, including engineering, economics, and aprendizaje automático, for optimizing functions that may be difficult to solve using traditional methods.