swartzism said:I am trying to write a differential evolution in FORTRAN, but I am a bit confused by the concept. I've read through Storn and Price's explanation of the concept, but I'm still not getting it. Can anyone offer a dumb-ed down version of what exactly a differential evolution is?
Differential Evolution is a type of evolutionary algorithm that is used to optimize multi-dimensional functions. It is a population-based method that mimics the process of natural selection and evolution to find the optimal solution to a problem.
Differential Evolution works by creating a population of potential solutions to a problem and then using a combination of mutation, crossover, and selection operations to produce new generations of solutions. The algorithm aims to improve the solutions in each generation until the optimal solution is found.
One of the main advantages of Differential Evolution is that it is a simple and easy-to-understand algorithm. It also has a good convergence rate and can handle noisy or non-linear problems. Additionally, it does not require any gradient information, making it suitable for a wide range of optimization problems.
Differential Evolution may not perform well on problems with a large number of variables or with highly complex landscapes. It also requires careful selection of parameters and can be sensitive to the choice of mutation strategies. In some cases, it may also suffer from premature convergence.
Differential Evolution has been successfully applied in various fields such as engineering, economics, and machine learning. It is commonly used for optimization problems such as parameter estimation, feature selection, and pattern recognition. It has also been used in designing neural networks and for training machine learning models.