Multi-Objective Evolutionary Rule-Based Classification with Categorical Data

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

Algorithm 1

(μ + λ)

strategy for multi-objective optimization.

Require:

T > 1

{Number of generations}
Require:

N > 1

{Number of individuals in the population}

1:
Initialize P with N individuals
2:
Evaluate all individuals of P
3:
$t \leftarrow 0$
4:
while $t < T$ do
5:
$Q \leftarrow \emptyset$
6:
$i \leftarrow 0$
7:
while $i < N$ do
8:
Parent1← Binary tournament selection from P
9:
Parent2← Binary tournament selection from P
10:
Child1, Child2←Crossover(Parent1, Parent2)
11:
Offspring1←Mutation(Child1)
12:
Offspring2←Mutation(Child2)
13:
Evaluate Offspring1
14:
Evaluate Offspring2
15:
$Q \leftarrow Q ⋃ \{Offspring 1, Offspring 2\}$
16:
$i \leftarrow i + 2$
17:
end while
18:
$R \leftarrow P ⋃ Q$
19:
$P \leftarrow$ N best individuals from R according to the rank-crowding function in population R
20:
$t \leftarrow t + 1$
21:
end while
22:
return Non-dominated individuals from P