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. 2018 Sep 7;20(9):684. doi: 10.3390/e20090684

Algorithm 5 Initialize population.

Require:

p > 0

{Number of categorical input attributes}
Require:

v_{1}, \dots, v_{p}

,

v_{j} > 1

,

j = 1, \dots, p

{Number of categories for the input attributes}
Require:

w > 1

, {Number of classes for the output attribute}
Require:

δ > 0

{Number of crossing operators}
Require:

ϵ > 0

{Number of mutation operators}
Require:

M_{m a x} \geq w

{Maximum number of rules}
Require:

N > 1

{Number of individuals in the population}

1:
$P \leftarrow \emptyset$
2:
for $k = 1$ to N do
3:
$I \leftarrow$ new Individual
4:
if $k \leq M_{m a x} - w + 1$ then
5:
$M_{I} \leftarrow k + w - 1$
6:
else
7:
$M_{I} \leftarrow$ Int Random(w, $M_{m a x}$ )
8:
end if
9:
{Random rule $R_{i}^{I}$ }
10:
for $i = 1$ to $M_{I}$ do
11:
{Random integer values associated with the antecedents}
12:
for $j = 1$ to p do
13:
$b_{i j}^{I} \leftarrow$ Random(1, $v_{j}$ )
14:
end for
15:
{Random integer value associated with the consequent}
16:
if $i < w$ then
17:
$c_{i}^{I} = j$
18:
else
19:
$c_{i}^{I} \leftarrow$ Random(1,w)
20:
end if
21:
end for
22:
{Random integer values for adaptive variation}
23:
$d_{I} \leftarrow$ Random(0, $δ$ )
24:
$e_{I} \leftarrow$ Random(0, $ϵ$ )
25:
$P \leftarrow P \cup I$
26:
end for
27:
returnP