Ultimate Algorithmic Trading System

roulette(i) = Rnd(1#)
If roulette(i) <= crossRate Then
crossArr(i) = 1
End If
Next i
crossCnt = 0
For i = 0 To 5
Next i
'parentsList is a simple class
'similar to a point class with 2 coordinates
'total of six possible matchings
parentsList(i).mom = 999
parentsList(i).dad = 999
If crossArr(i) <> 0 Then
crossCnt = crossCnt + 1
'found mom but not dad - yet
'store mom in parentsList(i)
parentsList(i).mom = i
foundMate = False
cindex = i
Do While Not (foundMate)
Loop
End If
cindex = cindex + 1
If cindex > 5 Then cindex = 0
If crossArr(cindex) <> 0 Then
'found dad - store dad in parentsList(i)
End If
parentsList(i).dad = cindex
foundMate = True
In nature, a child will receive genetic material or traits from both parents. Some
children will receive more traits from the father than the mother, and vice versa.
How is this determined? In nature, who knows, but inside a computer, we can
discriminate how much the father and mother will contribute to the offspring.
Here again, an RNG saves the day. Each chromosome holds four different genes
(numbers), so we need to know how many of the four numbers will be passed on
by the father and mother to the new offspring. This is accomplished by randomly
calculating the split point where the numbers will be taken from both parents. In
other words, if the split equals two, then the mother chromosome will contribute her
first two numbers and the father chromosome will contribute his last two numbers.
There are four genes in each chromosome, so the random number generator will
generate a number between one and four. Figure 8.4 shows how the sharing of
genetic material is accomplished when the split point is one.
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GENETIC OPTIMIZATION, WALK FORWARD
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