通用算法与传统算法的区别

Question

I want difference points between generic algorithm and traditional algorithm . please need some points.

Answer 1

With a little research, i've found a lot of articles. One of the key points is that:

A standard genetic algorithm deals with a set (a population) of possible solutions (individuals) of a problem. Each individual is a point in the search space, so we can think of the genetic algorithm as a multi-point optimization technique for multi-dimensional spaces. Usually, the size of the population is in the range from 20 to 200 or 300. The majority of traditional optimization methods explores 1, 2, or 3 points in the search space on each iteration.

Traditional methods require a starting point to begin the optimization. Often the quality of the final solution is very dependent upon the position of this starting point in the search space. The choice of a starting point plays a significant role in finding a good solution to the problem with a large number of local optima. Genetic algorithms, which offer many solutions and can search multiple points simultaneously, do not suffer as much from this drawback.

And also:

Genetic algorithms use probabilistic transition rules, not deterministic rules

I suggest you to do some research, i've found plenty of articles.

You can start with this article.