Multiplexers are logic circuits frequently used in communications and input/output operations for transmitting a number of separated signals simultaneously over a single channel.
The task of the 6bit Boolean multiplexer is to decode a 2bit binary address (00, 01, 10, 11) and return the value of the corresponding data register
(d_{0}, d_{1}, d_{2}, d_{3}). Thus, the 6multiplexer is a function of six activities: two,
a_{0} and a_{1}, determine the address, and four,
d_{0} through d_{3}, determine the answer.
There are 2^{6} = 64 possible combinations for the six arguments of the 6multiplexer function and, for this problem, the entire set of 64 combinations was used as the selection environment. The rule table for the 6multiplexer is shown in
Table 5.2 and the fitness was evaluated by equation
(3.2). Thus, f_{max} = 64.
Table 5.2
Lookup table for the 6multiplexer. The output bits are given in lexicographic order starting with 000000 and finishing with 111111.
In order to simplify the analysis, I chose a rather compact chromosomal organization and excluded the “Q” function from the function set. Thus, for this problem,
F = {U, D, T}, where “U” represents a function with connectivity one;
T = {a, b, c, d, e, f}, representing, respectively,
{a_{0}, a_{1}, d_{0}, d_{1},
d_{2}, d_{3}}; and W = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, each taking values from the interval [2, 2].
For the experiment summarized in the first column of Table
5.3, unigenic chromosomes were chosen in order to simulate more faithfully a neural network. One of the most parsimonious solutions found has a total of 32 nodes and is shown in
Figure 5.6.
Table 5.3
Parameters for the 6multiplexer problem using a unigenic system (US) and a multigenic system
(MS).

US 
MS 
Number
of runs 
100 
100 
Number
of generations 
2000 
2000 
Population
size 
50 
50 
Number
of fitness cases 
64 (Table
5.2) 
64 (Table
5.2) 
Function
set 
(U D
T)_{3} 
(U D
T)_{3} 
Terminal
set 
a b c
d e f 
a b c
d e f 
Linking
function 
 
O 
Weights
array length 
10 
10 
Weights
range 
[2,
2] 
[2,
2] 
Head
length 
17 
5 
Number
of genes 
1 
4 
Chromosome
length 
103 
124 
Mutation
rate 
0.044 
0.044 
Intragenic
twopoint recombination rate 
0.6 
0.6 
Gene
recombination rate 
 
0.1 
Gene
transposition rate 
 
0.1 
IS
transposition rate 
0.1 
0.1 
IS
elements length 
1,2,3 
1,2,3 
RIS
transposition rate 
0.1 
0.1 
RIS
elements length 
1,2,3 
1,2,3 
Weights
mutation rate 
0.002 
0.002 
Dw
specific transposition rate 
0.1 
0.1 
Dw
specific IS elements length 
2,3,5 
2,3,5 
Success
rate 
4% 
6% 
Obviously, we could explore the multigenic nature of GEP chromosomes and evolve multigenic neural networks. The solutions found are, however, structurally more constrained as we have to choose some kind of linking function to link the subNNs encoded by each gene. In this case, the Boolean function OR was chosen to link the subNNs. (If the mixing of OR with “U”, “D”, and “T” functions is confusing, think of OR as a function with connectivity two and weights and thresholds all equal to one, and you would have a neural net OR.)
Figure 5.6. A perfect solution to the 6multiplexer function discovered with GEP designed neural networks.
a) Its chromosome and corresponding array of weights. b) The fully expressed neural network encoded in the chromosome.
In the experiment summarized in the second column of Table
5.3, four genes posttranslationally linked by OR were used. The first solution found in this experiment is shown in
Figure 5.7. Note that some of the weights in genes 1 and 2 have identical values, and that the same happens for genes 3 and 4. This most probably means that these genes share a common ancestor.
Figure 5.7. A perfect solution to the 6multiplexer problem encoded in a fourgenic chromosome.
a) Its chromosome with each gene shown separately. W_{1}W_{4} are the arrays containing the weights of each gene.
b) The subNNs codified by each gene. In this perfect solution, the subNNs are linked by OR.
