Ultimate Algorithmic Trading System

Mutating Chromosomes—Using VBA Excel
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GENETIC OPTIMIZATION, WALK FORWARD
mutRate = 0.1 ' rate of mutation
numMutations = Int(mutRate * 24) ' 24 genes available for
mutation
Call printDivider(lineCount, 4)
lineCount = lineCount + 1
Randomize
For i = 0 To numMutations - 1 'loop for 2 mutations
Next i
chromoMut(i) = Int((24 - 0 + 1) * Rnd + 0) 'which
genes to mutate
genLocRow = Int(chromoMut(i) / 4) 'locate the gene
in the matrix
If genLocRow = 0 Then genLocRow = 1
genLocCol = 4 - chromoMut(i) Mod 4
chromoMutCoOrds(i, 0) = genLocRow - 1 'found gene row
chromoMutCoOrds(i, 1) = genLocCol + 1 'found gene
Randomize
column
rndVal = Int((24 - 0 + 1)*Rnd + 0) 'randomize the new gene value
chromo(genLocRow - 1, genLocCol - 1) = rndVal
After selection, reproduction, and mutation a new generation has been created.
Here are the second generation’s chromosomes and respective fitness values after
subtracting 40:
Chromosome (0) = [16,05,13,28] = 53
Chromosome (1) = [13,08,22,20] = 75
Chromosome (2) = [00,30,30,04] = 114
Chromosome (3) = [16,05,13,28] = 53
Chromosome (4) = [10,30,30,04] = 124
Chromosome (5) = [00,20,05,15] = 30
It seems redundant to state this again, but here is another example of how genetic
algorithms mimic nature by not only creating inferior offspring but also creating
an inferior generation. However, since these generations live inside of a computer,
they are short-lived and the computer moves quickly onto the next generation until
a solution is found. This GA solved the problem in 13 generations after a mutation
occurred. Eventually, chromosome (2) with genes [13,0,4,15] was chosen as the
solution. Test it!
Test ∶ 13 + 2 ×(0)+3 ×(4)+15 = 40
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