Pseudo-random numbers are chosen with equal probability from a finite set of numbers. The chosen numbers are not completely random because a definite mathematical algorithm is used to select them, but they are sufficiently random for practical purposes. The current implementation of the Random class is based on Donald E. Knuth's subtractive random number generator algorithm. For more information, see D. E. Knuth. "The Art of Computer Programming, volume 2: Seminumerical Algorithms". Addison-Wesley, Reading, MA, second edition, 1981.
The random number generation starts from a seed value. If the same seed is used repeatedly, the same series of numbers is generated. One way to produce different sequences is to make the seed value time-dependent, thereby producing a different series with each new instance of Random. By default, the parameterless constructor of the Random class uses the system clock to generate its seed value, while its parameterized constructor can take an Int32 value based on the number of ticks in the current time. However, because the clock has finite resolution, using the parameterless constructor to create different Random objects in close succession creates random number generators that produce identical sequences of random numbers. The following example illustrates that two Random objects that are instantiated in close succession generate an identical series of random numbers.
Dim bytes1(99), bytes2(99) As Byte
Dim rnd1 As New Random()
Dim rnd2 As New Random()
rnd1.NextBytes(bytes1)
rnd2.NextBytes(bytes2)
Console.WriteLine("First Series:")
For ctr As Integer = bytes1.GetLowerBound(0) to bytes1.GetUpperBound(0)
Console.Write("{0, 5}", bytes1(ctr))
If (ctr + 1) Mod 10 = 0 Then Console.WriteLine()
Next
Console.WriteLine()
Console.WriteLine("Second Series:")
For ctr As Integer = bytes2.GetLowerBound(0) to bytes2.GetUpperBound(0)
Console.Write("{0, 5}", bytes2(ctr))
If (ctr + 1) Mod 10 = 0 Then Console.WriteLine()
Next
' The example displays the following output to the console:
' First Series:
' 97 129 149 54 22 208 120 105 68 177
' 113 214 30 172 74 218 116 230 89 18
' 12 112 130 105 116 180 190 200 187 120
' 7 198 233 158 58 51 50 170 98 23
' 21 1 113 74 146 245 34 255 96 24
' 232 255 23 9 167 240 255 44 194 98
' 18 175 173 204 169 171 236 127 114 23
' 167 202 132 65 253 11 254 56 214 127
' 145 191 104 163 143 7 174 224 247 73
' 52 6 231 255 5 101 83 165 160 231
'
' Second Series:
' 97 129 149 54 22 208 120 105 68 177
' 113 214 30 172 74 218 116 230 89 18
' 12 112 130 105 116 180 190 200 187 120
' 7 198 233 158 58 51 50 170 98 23
' 21 1 113 74 146 245 34 255 96 24
' 232 255 23 9 167 240 255 44 194 98
' 18 175 173 204 169 171 236 127 114 23
' 167 202 132 65 253 11 254 56 214 127
' 145 191 104 163 143 7 174 224 247 73
' 52 6 231 255 5 101 83 165 160 231
byte[] bytes1 = new byte[100];
byte[] bytes2 = new byte[100];
Random rnd1 = new Random();
Random rnd2 = new Random();
rnd1.NextBytes(bytes1);
rnd2.NextBytes(bytes2);
Console.WriteLine("First Series:");
for (int ctr = bytes1.GetLowerBound(0);
ctr <= bytes1.GetUpperBound(0);
ctr++) {
Console.Write("{0, 5}", bytes1[ctr]);
if ((ctr + 1) % 10 == 0) Console.WriteLine();
}
Console.WriteLine();
Console.WriteLine("Second Series:");
for (int ctr = bytes2.GetLowerBound(0);
ctr <= bytes2.GetUpperBound(0);
ctr++) {
Console.Write("{0, 5}", bytes2[ctr]);
if ((ctr + 1) % 10 == 0) Console.WriteLine();
}
// The example displays the following output to the console:
// First Series:
// 97 129 149 54 22 208 120 105 68 177
// 113 214 30 172 74 218 116 230 89 18
// 12 112 130 105 116 180 190 200 187 120
// 7 198 233 158 58 51 50 170 98 23
// 21 1 113 74 146 245 34 255 96 24
// 232 255 23 9 167 240 255 44 194 98
// 18 175 173 204 169 171 236 127 114 23
// 167 202 132 65 253 11 254 56 214 127
// 145 191 104 163 143 7 174 224 247 73
// 52 6 231 255 5 101 83 165 160 231
//
// Second Series:
// 97 129 149 54 22 208 120 105 68 177
// 113 214 30 172 74 218 116 230 89 18
// 12 112 130 105 116 180 190 200 187 120
// 7 198 233 158 58 51 50 170 98 23
// 21 1 113 74 146 245 34 255 96 24
// 232 255 23 9 167 240 255 44 194 98
// 18 175 173 204 169 171 236 127 114 23
// 167 202 132 65 253 11 254 56 214 127
// 145 191 104 163 143 7 174 224 247 73
// 52 6 231 255 5 101 83 165 160 231
This problem can be avoided by creating a single Random object rather than multiple ones.
To improve performance, create one Random object to generate many random numbers over time, instead of repeatedly creating a new Random objects to generate one random number.
To generate a cryptographically secure random number suitable for creating a random password, for example, use a class derived from System.Security.Cryptography..::.RandomNumberGenerator such as System.Security.Cryptography..::.RNGCryptoServiceProvider.
Notes to Inheritors: In the .NET Framework versions 1.0 and 1.1, a minimum implementation of a class derived from Random required overriding the Sample method to define a new or modified algorithm for generating random numbers. The derived class could then rely on the base class implementation of the Random..::.Next()()(), Random..::.Next(Int32), Random..::.Next(Int32, Int32), NextBytes, and NextDouble methods to call the derived class implementation of the Sample method.
In the .NET Framework version 2.0 and later, the behavior of the Random..::.Next()()(), Random..::.Next(Int32, Int32), and NextBytes methods have changed so that these methods do not necessarily call the derived class implementation of the Sample method. As a result, classes derived from Random that target the .NET Framework 2.0 and later should also override these three methods.