differential-evolution

In Differential Evolution Algorithm for optimization problems. There are three evolutionary processes involved, that is mutation crossing over and selection I am just a beginner but I have tried removing the crossing over process and there is no significant difference result from the original algorithm. So what is the importance of crossing over in Differential Evolution Algorithm? If you don't use crossover may be your algorithm just explore the problem search space and doesn't exploit it. In general an evolutionary algorithm succeeds if it makes good balance between exploration and

Can someone please explain the Differential Evolution method? The Wikipedia definition is extremely technical. A dumbed-down explanation followed by a simple example would be appreciated :) Here's a simplified description. DE is an optimisation technique which iteratively modifies a population of candidate solutions to make it converge to an optimum of your function. You first initialise your candidate solutions randomly. Then at each iteration and for each candidate solution x you do the following: you produce a trial vector: v = a + ( b - c ) / 2, where a, b, c are three distinct candidate

From what I've read so far they seem very similar. Differential evolution uses floating point numbers instead, and the solutions are called vectors? I'm not quite sure what that means. If someone could provide an overview with a little bit about the advantages and disadvantages of both. Well, both genetic algorithms and differential evolution are examples of evolutionary computation. Genetic algorithms keep pretty closely to the metaphor of genetic reproduction. Even the language is mostly the same-- both talk of chromosomes, both talk of genes, the genes are distinct alphabets, both talk of