TELKOMNIKA ISSN: 1693-6930
Application of A Self-adaption Dual Population Genetic Algorithm in… Cheng Zhang 241
3.2. The Process of Genetic Algorithm
The genetic operations of genetic algorithm in the entire evolution process are random, but the characteristic it presents is full search. It can effectively use the previous information to
predict the optimization point set with improved expected performance in the next generation. After the continuous evolution from generation to generation, it is finally converged to the
individual which can adapt to the environment at most and the optimal solution to the problem can be obtained. Genetic algorithm involves five elements: parameter coding, setting of initial
population, design of fitness function, design of genetic operation and setting of control parameters [10]. The operations of genetic algorithm are as follows:
1 Selection Selection operation combines elite selection and roulette wheel selection. At first, it
directly copies several elite individuals to the population in the next generation and select the rest individuals with roulette wheel method. In this way, it can not only preserve the excellent
individuals in the population, but also protect the diversity of the individuals in the population. 2 Crossover and Mutation
In the genetic operations, perform crossover and mutation operations at the crossover probability
c
P and mutation probability
m
P . After crossover and mutation operations, conduct
validation test on the newly-generated individuals to check whether the solutions of the new individuals meet the sequence constraints. If so, it proves that these new individuals are
effective; if not, they are invalid and adjustments needs to be made on them. Redistribute some operations to make them effective genes [11, 12].
The basic flowchart of genetic algorithm is indicated as Figure 2.
Figure 2. Basic flowchart of genetic algorithm
4. Design of Multi-objective Genetic Algorithm Based on Self-adaption and Dual Population Strategy
In the genetic evolution, the differences among the fitness of the individuals in the population vary from the differences of the evolution. At the early evolution, the difference is big,
but it becomes small in the late evolution. In order to guarantee that the individuals can be selected in early evolution to preserve the diversity of the individuals in the population and
highlight the excellent individuals in the late evolution to improve the competitiveness of the individuals, this paper has come up with a Multi-objective Dual Population Genetic Algorithm
MODPGA. The steps of MODPGA are as follows [13, 14]:
1 When the variable part of the individual
1
1,...,
l
i n
j l
l
X j
n
in the population I 1,...,
ui u
P i n
mutates, randomly select individuals to generate the variable part of the mutation
vector from the variable population the size is
u
n .
2 When the variable part
1
l
i n
j l
X
of the individual
1
1,...,
l
i n
j l
l
X j
n
in the population
1,...,
ui u
P i n
mutates, randomly select individuals to generate the variable part of
i u
X
u
P
ISSN: 1693-6930
TELKOMNIKA Vol. 14, No. 1, March 2016 : 238 – 244
242 the mutation vector from the variable population II
l
P the size is
l
n without being limited to the
population I
ui
P .
3 In order to protect the evolution structure of population I, MODPGA updates the population I
ui
P in a dynamic manner so as to realize the dynamic update of the entire
population I. 4 Preserve the non-dominant elite individuals with the level of
and by
using external archival strategy.
Figure 3. Flowchart of MODPGA The process for MODPGA to conduct mutation and crossover operations to generate
new experimental individuals includes two stages. Stage I: Randomly select three different individuals from the existing objective individual
in
u
n different variable populations
to perform variable mutation and crossover operations of population I and generate the variable part of the experimental individuals
1
1,...,
l
i n
j l
l
U j
n
. Stage II: As for the variable part
1
l
i n
j l
X
of all the individuals in population
ui
P ,
randomly select individuals to perform mutation and crossover operations from the entire population
the size is
u
n and generate the variable part of the experimental individual
1
l
i n
j
U
. The flowchart of MODPGA is indicated as Figure 3.
5. Performance Test and Analysis of the Algorithm in This Paper
