Evolução Diferencial (DE) is a powerful, population-based algoritmo de otimização 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 problema de otimização. DE operates by combining existing solutions to generate new candidate solutions, which are then evaluated based on a predefined objective function.
O algoritmo segue estas etapas principais:
- Inicialização: A population of candidate solutions is randomly generated within the defined search space.
- Mutação: 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.
- CrossOver: 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 quanto da nova solução é misturada com a original.
- Seleção: The trial solution is compared to the original candidate solution. If the trial solution has a better fitness valor, ela substitui a solução original na população.
Este ciclo de mutação, crossover e seleção continua até que um critério de parada seja atingido, como um número máximo de gerações ou um limite de erro aceitável.
Differential Evolution is known for its simplicity and effectiveness, making it a popular choice in various fields, including engineering, economics, and aprendizado de máquina, for optimizing functions that may be difficult to solve using traditional methods.