Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set Network

Table 3

Iterative realization of genetic algorithm in neural set network.

Code number	Genetic algorithm in neural set network	Text content
1	Slices are suitable for	#include <algorithm>
2	not all time goes	#include <functional>
3	During the day	Using namespace std;
4	The set of	Sort (a, a + 5, less <int>());
5	as shown in	Sort (a, a + 5, greater <int>());
6	A total of	Template <class T> inline int
7	Each experiment was	Int a[] = {1, 4, 3, −13734, 1e3};
8	of iterations epochs	Qsort (a, 5, Greater <int>);
9	The neural set network is	Qsort (a, 5, Less <int>);
10	Which the program runs	Fill (a, a + 105, 0);
11	The initial weight of	Fill (a, a + 105, 0x7fffffff)
12	in the index area	#include <cstring>
13	The neural set network in table	Char s1[] = “Hello,” “World;”
14	All functions	Using namespace std;
15	In the model of	String s1, s2 = “World;”
16	The maximum number	Freopen (“in.in,” “r,” stdin);
17	Unconventional English course learning	Freopen (“out.out,” “w,” stdout);
18	Parameters of the learning rate	Fclose (stdin); fclose (stdout);
19	Refer to	Fprintf (out, “%d,” a);