differential-evolution

问题 I am using the differential evolution optimizer in scipy and I don't understand the intuition behind the tol argument. Specifically is say in the documentation: tol: float, optional When the mean of the population energies, multiplied by tol, divided by the standard deviation of the population energies is greater than 1 the solving process terminates: convergence = mean(pop) * tol / stdev(pop) > 1 What does setting tol represent from a user perspective? 回答1: Maybe the formula in the

问题 I am trying to find the global minimum of a function using differential_evolution from scipy.optimize. As explained in the scipy reference guide, I should set in the options: updating='deferred',workers=number of cores However, when I run the code, it freezes and does nothing. How can I solve this issue, or is there any better way for parallelizing the global optimizer? The following is in my code: scipy.optimize.differential_evolution(objective, bnds, args=(), strategy='best1bin', maxiter

问题 #I EDITED MY ORIGINAL POST in order to put a simpler example. I use differential evolution (DE) of Scipy to optimize certain parameters. I would like to use all the PC processors in this task and I try to use the option "workers=-1" The codition asked is that the function called by DE must be pickleable. If I run the example in https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html#scipy.optimize.differential_evolution, the optimisation works. from

来源： https://stackoverflow.com/questions/64635666/differential-evolution-gets-error-with-workers-1

来源： https://stackoverflow.com/questions/60854268/stop-scipy-differential-evolution-after-a-time-threshold

问题 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? 回答1: If you don't use crossover may be your algorithm just explore the problem search space and doesn't exploit

问题 I am trying to use differential evolution to optimize availability based on cost. However, I have three unknown parameters (a, b, c) here and I can define the range using bounds. However, I want to define additional constraint as a+b+c <= 10000. I am using python to do this and I tried to use an option "args" within differential evolution but it did not work. Any information will be appreciated. 回答1: Here is a hack. I used the last example from the documentation and constrained the sum(x) > 4

问题 I'm trying to run an optimization with scipy.optimize.differential_evolution. The code calls for bounds for each variable in x. But I want to a solution where parts of x must be integers, while others can range freely as floats. The relevant part of my code looks like bounds = [(0,3),(0,3),(0,3),???,???] result = differential_evolution(func, bounds) What do I replace the ???'s with to force those variables to be ints in a given range? 回答1: As noted in the comments there isn't direct support

问题 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. 回答1: 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

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 years ago . explain all updates in the basic algorithm of differential evolution. i am not able to find all versions of this algorithm. explain all versions of this algorithm as a survey and i am not clearly understand the theory behind this algorithm as given in the Wikipedia. Wikipedia also define only basic