Ultimate Algorithmic Trading System

This equation involves four unknowns and multiple solutions and can be easily
solved by trial and error. We could simply set a, c, and d equal to zero and set b
equal to 20 and be done with it. Remember, this is an oversimplified problem to
demonstrate the four core concepts of GA.
■ Population
There are four variables in our equation so continuing to borrow from biology let’s
create six chromosomes consisting of four values or genes that will be substituted
into the variables a, b, c, and d. Unlike real chromosomes, these will simply
be placeholders for the four different values of the variables (genes). Figure 8.2
illustrates our six amoeba-looking genes.
So the population will consist of six chromosomes that have four different genes
that can be swapped or mutated. Why six chromosomes instead of four, you may
ask? Just be patient; we will get to that. The initial population of chromosomes will
be generated using a random number generator (RNG). Each chromosome will be
defined by its genetic value, and these are the values that will be randomized. Values
between 0 and 30 for each gene will be initially generated in a random fashion. You
will soon discover that GA relies heavily on random numbers. This is yet another
similarity with nature—randomness. Here is the initial population generated by the
VBA software and its RNG:
Genes
Generation 1: Chromosome(0) = [10,11,20,19]
Chromosome(1) = [13,08,15,20]
Chromosome(2) = [00,20,05,09]
Chromosome(3) = [07,27,05,14]
Chromosome(4) = [10,30,30,04]
Chromosome(5) = [16,05,13,28]
10
11
20
19
13
08
15
20
00
20
05
09
07
27
05
14
10
30
30
04
16
05
13
28
231
GENETIC OPTIMIZATION, WALK FORWARD
FIGURE 8.2
An illustration of our chromosomes with different genetic values.
www.rasabourse.com