https://doi.org/10.31449/inf.v46i3.4049                                                                                      Informatica 46 (2022) 365-372     365 
Application of Interactive Genetic Algorithm in Landscape Planning 
and Design 
 
Boyang Li
1*
, Ashutosh Sharma
2
 
1
Jiangxi University of Technology, College of Art and Design, Nanchang, Jiangxi 330003, China  
2
School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248171, India 
Emails: boyangli7@126.com, ashutosh.sharma@ddn.upes.ac.in  
 
Keywords: Interactive; Genetic algorithm; Garden landscape; Space environment; Visual model; landscape spatial 
environment design. 
 
Received: February 24, 2022 
 
This article aims at improving the design effect of garden landscape space environment and optimizes 
the structure of garden landscape space environment. An optimization design method of garden 
landscape space environment based on interactive genetic algorithm is proposed in this article by 
designing a landscape space environment design with image visual feature space distributed monitoring 
model and fuzzy pixel area feature fusion reconstruction model. The landscape space environment 
design of the image by multistage decomposition and pixel gray characteristics has been established 
landscape space environment design image visual feature reconstruction model. The method proposed 
in this article combines the block area template matching method with landscape space environment 
design of the image features visual reconstruction. The visual space distributed detection is done with 
information fusion using the similarity model reconstruction for landscape space environment design of 
image visual perception process of information fusion. In order to extract fuzzy characteristic landscape 
space environment design of the image, interactive genetic algorithm is used to realize the quality 
assessment of landscape art information fusion perception and visual reconstruction. The simulation 
results show that compared with the traditional method, the visual reconstruction quality of landscape 
spatial environment design image processed by this method is better along with higher image 
recognition accuracy, and the output signal-to-noise ratio is improved by 14.6%. The experimental 
results prove that the introduction of interactive genetic algorithm in landscape planning and design can 
effectively solve the problems of multi-level feature decomposition and pixel feature separation in the 
process of landscape design. The proposed method achieves better optimization of landscape spatial 
environment structure, and achieves good landscape spatial environment design effect. 
 
Povzetek: Za snovanje prostorskih krajinskih načrtov, npr. vrtov,  je uporabljen interaktivni genetski 
algoritem.
  
1 Introduction  
The development of computer vision information 
processing technology, using visual image processing 
method to analyze and extract the features of garden 
landscape space environment, and establishing the visual 
reconstruction model of garden landscape space 
environment design image can effectively improve the 
artistic feature identification and fusion reconstruction 
ability of garden landscape space environment. In recent 
years, the feature reconstruction technique of landscape 
spatial environment art has attracted extensive attention 
from scholars at home and abroad. Compared to 
traditional landscape design method of space 
environment, the introduction of artificial intelligence 
technology, such as interactive genetic algorithm 
(depicted in Figure 1) can effectively extract the 
geometrical characteristics of the landscape space 
environment design image analysis model.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
By machine vision characteristics such as 3-d 
reconstruction method, realization of the landscape space 
environment design and visual image reconstruction, the 
ability of geometric characteristic of visual recognition 
can be improved, thus, obtaining high-quality structural 
models [1]. 
1 - 2 - 3 - 4 - 5 - 6 - 7 - 8 
1 - 2 - 3 - 4 - 6 - 5 - 7 - 8 
2 - 3 - 6 - 5 - 4 - 7 - 8 - 1 
Figure 1: Interactive genetic algorithm 

366     Informatica 46 (2022) 365-372                                                                                                                                    B.  Li et al. 
1.1 Literature review  
In view of this research problem, Shen proposed 
that the feature reconstruction of garden landscape spatial 
environment art is based on the visual reconstruction of 
garden landscape spatial environment design image, and 
the distributed fusion of visual image and binary 
recognition method can be used for the visual 
reconstruction processing of garden landscape spatial 
environment design image [2]. Based on the existing 
classification, Tamene et al. introduced the related 
concepts, development and role of ARTIFICIAL 
intelligence in landscape architecture research, and 
pointed out the specific application and existing 
problems of various artificial intelligence methods in 
landscape architecture analysis, design and evaluation 
[3]. Murgante et al. divided the mainstream 
ARTIFICIAL intelligence methods in landscape 
architecture research into artificial life, intelligent 
random optimization and machine learning, discussed the 
principle, development and characteristics of typical 
algorithms in each category, and then discussed the 
necessity of establishing a hybrid intelligence system and 
its future development prospect, as shown in Table 1 [4]. 
According to the attributes and applications of 
ARTIFICIAL intelligence, Ohman analyzed its 
limitations in the research of landscape architecture and 
pointed out the development trend of intelligent design of 
landscape architecture [5]. Smith proposed a CA model 
for landscape design based on inertial weight particle 
swarm optimization, introduced swarm intelligence into 
landscape design modeling, reduced the uncertainty of 
simulation, and established an efficient CA model to 
simulate landscape dynamics [6]. Anagnostopoulos and 
Mamanis used the improved Logics-CA mathematical 
model to simulate and predict the characteristics of 
landscape evolution space process under three conditions 
(historical extrapolation, endogenous development and 
exogenous development) in Tianjin Coastal area from 
2011 to 2020, so as to further obtain the elements 
affecting landscape design and master the development 
process of landscape design [7]. Cho et al. proposed the 
use of interactive genetic algorithm to express the cycle 
iteration relationship of design activities in view of the 
complex iterative cycle in garden engineering design, so 
as to clearly reflect the data mutual extraction between 
design activities [8]. Eikelboom et al. proposed a DSM-
GA algorithm compiled based on the critical path 
Method (CPM), and applied the genetic algorithm to the 
design activity matrix to find a better order arrangement 
of design activities, so as to optimize the design iteration 
and shorten the design period [9]. Venema and Calamai 
extracted the geometric feature analysis model of garden 
landscape spatial environment design image, and realized 
garden landscape spatial environment design and image 
visual reconstruction through the 3d reconstruction 
method of computer vision features [10]. Banyai used 
pixel tracking and fusion technology to construct key 
feature quantity of landscape spatial environment design 
image, and realized landscape spatial environment design 
and optimization recognition [11]. 
1.2 Contribution 
On the basis of the current research, this paper 
proposes an optimization design method of landscape 
space environment based on interactive genetic 
algorithm. A landscape space environment design is built 
in this article for image visual feature space distributed 
detection and fuzzy pixel area feature fusion 
reconstruction. The classification of artificial intelligence 
in landscape architecture research is depicted in Figure 2.  
The proposed model achieves a similarity 
information fusion model is adopted to improve the 
landscape space environment design in the process of 
visual image reconstruction garden art landscape 
perception and block template matching area information 
fusion. This article aims at improving the design effect of 
garden landscape space environment and optimize the 
structure of garden landscape space environment. An 
optimization design method of garden landscape space 
environment based on interactive genetic algorithm is 
proposed in this article by designing a landscape space 
environment design with image visual feature space 
distributed monitoring model and fuzzy pixel area feature 
fusion reconstruction model. The method proposed in 
this article combines the block area template matching 
method with landscape space environment design of the 
image features visual reconstruction. The visual space 
distributed detection is done with information fusion 
using the similarity model reconstruction for landscape 
space environment design of image visual perception 
process of information fusion. The proposed method 
achieves better optimization of landscape spatial 
environment structure, and achieve good landscape 
spatial environment design effect. 
The rest of this article is arranged as: section 2 
presents the research methods depicting the visual 
sampling and fusion of landscape spatial environment 
design. Section 3 presents the research results and 
discussion followed by conclusion in section 4.  

Application of Interactive Genetic Algorithm in Landscape…                                                   Informatica 46 (2022) 365-372     367 
 
Figure 2: Classification of artificial intelligence in landscape architecture research 
 
 
2 Research methods 
2.1 Visual sampling and fusion of 
landscape spatial environment design  
The flowchart of visual sampling and fusion of 
landscape spatial environment design is presented in 
Figure 3. 
 
 
Figure 3: Flowchart of landscape spatial environment 
design 
 
The system provides the genetic operation 
applications in urban landscapes for evolving the input 
variabilities in position of walls, their heights and 
building structure. The weighted estimate of feature 
matching is done using spare features for image 
reconstruction which is detailed in further subsection.  
 
A. Landscape space environment design image 
collection 
In order to achieve the landscape space environment 
design based on interactive genetic algorithm (GA) and 
visual image reconstruction, to build landscape space 
environment design of the image pixel space fusion 
model. The feature matching method is adopted to 
improve the landscape space environment design and  
 
image feature detection [12], for the sparse feature of the 
landscape space environment design image 
reconstruction. The Atanassov extension method was 
used to match feature points of landscape spatial 
environment design images, and the template matching 
model for visual reconstruction of landscape spatial 
environment design images was constructed, as shown in 
Figure 4. 
 
Figure 4: Template matching model of landscape space 
environment design 
 
Assume that landscape space environment design of 
the image gray of pixel sets ( 𝐼 , 𝑗 ), as a pixel center, 
sharpening template block combination method is used to 
construct landscape space environment design of the 
image characteristics of visual reconstruction model. For 
the first 𝑘 is the belt in the acquisition of landscape space 
environment design of the image grey value 𝐼 𝑠𝑤 𝑘 , in gray 
pixel distribution characteristics of space, The gradient 
characteristic components of the corresponding 
landscape spatial environment design image are as 
follows: 
)
) , (
,...,
) , 1 (
,...,
) , 2 (
,
) , 1 (
(
1 1 1 1
c
j r I
c
j I
c
j I
c
j I
P
c
j
swk
c
j
swk
c
j
swk
c
j
swk
rk
   
   

 
(1) 
• Cellular Automation
• Agent-based Model and 
Multi-agent System
Artificial Life
• Genetic Algorithms
• Simulated Annealing
Intelligent Stochastic 
Optimization 
Processes
• Artificial Neural Network
• Convolutional Neural 
Network
• Decision Tree
• Random Forest
Machine Learning
Seed Point PN 
P2 
Straight Edge L 

368     Informatica 46 (2022) 365-372                                                                                                                                    B.  Li et al. 
)
) , (
,...,
) , (
,...,
) 2 , (
,
) 1 , (
(
1 i 1 i 1 i 1 i
r
c i I
r
j i I
r
i I
r
i I
P
r
swk
r
swk
r
swk
r
swk
ck
   
   

 
(2) 
Where 𝐶 is the column number of LGB vector 
quantization matrix of landscape spatial environment 
design image, and 𝑅 is motion fuzzy feature quantity. 
Based on the fusion reconstruction method of fuzzy pixel 
regional features, the pixel set of artistic feature 
distribution of landscape spatial environment was 
obtained, and the information reconstruction and three-
dimensional perception of landscape spatial environment 
design image were carried out to improve the ability of 
environment design.   
 
B. Image feature fusion and reconstruction 
model 
The spatial distributed detection model of visual 
feature of landscape spatial environment design image 
was constructed. Multi-stage feature decomposition and 
grey pixel feature separation of garden landscape spatial 
environment design image are carried out [13-15], and 
the visual feature reconstruction model of garden 
landscape spatial environment design image is 
established. The visual feature distribution of garden 
landscape spatial environment design image is as 
follows: 
 



p
j
j
x G x G
1
) ( ) (
 
(3) 
Adaptive fusion method was used to reconstruct the 
image vision of landscape design, and the edge vision 
reconstruction model of landscape spatial environment 
design was constructed [16, 17]. The fuzzy proximity 
function of landscape spatial environment image was 
obtained as follows: 
 


 
p
j
j
x G Ct x f x fitness
1
) ( ) ( ) ( ) (
 
 (4) 
 
It is assumed that the coordinate of garden 
landscape spatial environment design P N is (X PN, Y PN), 
then the coordinate of all garden landscape spatial 
environment design edge points (X k, y k) on L is 
compared with P N: when X K > X PN, i L = i L + 1; When x K 
< X PN, i L = i L-1; When X k = X PN, i L = i L + 0. The 
perception fusion model of landscape art information 
fusion is constructed, and the fitness function of 
landscape art information fusion is as follows:In this 
section various state-of-the-art work in the field of 
optimization design based on Computer-Aided 
architecture is presented.  







), ( 1
), (
) (
x rG
x f
x fitness
 
(5) 
Considering the gray level f of the garden landscape 
spatial environment design, the resolution model of the 
garden landscape spatial environment vision is 
constructed by using the gray level invariant moment 
feature decomposition method [18-20], and the visual 
feature reconstruction model of the garden landscape 
spatial environment is as follows: 
 


















 
  











 
 
))] (
2
(
))] ( 2 ( )[ ( 2
) , (
m in
m ax
m ax
) (
2
m in
) (
2
ln 2
max min






 


 


 
b b
f j
Ei
b b f j Ei b b j
f
e
f
e
K
e b a W
a
b b
f j
b b
f j
k i
u
 
(6) 
Where 𝑏 𝑎 = (1 − 𝑎 )(
1
𝑎𝑓
𝑚𝑎𝑥 −
𝑇 2
) ，𝐸𝑖 (. ) represents 
the recombination output of visual information features 
of garden landscape spatial environment. Combined with 
model recognition method, garden landscape design is 
carried out. 
2.2 Optimization of landscape design 
 
A. Landscape space environment vision 
After extracting all edge points on L, 𝛿 1
2
 is the local 
variance of landscape spatial environment design image, 
𝛿 𝑛 2
 is the optimization coefficient of landscape spatial 
environment design image. 𝛽 = max [
𝛿 1
2
−𝛿 𝑛 2
𝛿 1
2
, 0], using 
gradient descent method for visual landscape space 
environment of the regional block visual refactoring, 
make landscape space environment design of the image 
sparse eigenvalue meet 𝐶 ∈ 𝑆 , according to the sparse 
prior as a result, the environmental design of landscape 
space, image Fm the first m frames (𝑥 , 𝑦 ) (𝑥 , 𝑦 ) in the 
optimal visual reconstruction threshold. Based on the 
approximate sparse representation method, template 
matching of landscape spatial environment design image 
was carried out, and the matching coefficient was 
obtained as follows: 
 


   

否则 ,
,
*
i
j
i
g
y x i z Rs
g
 
(7) 
In the formula, 𝑅 is a standard constant. In 
combination with block area template matching method, 
landscape spatial environment design and distributed 
detection are carried out, and contour point matching 
model is used to extract edge features of landscape 
spatial environment design [21-23]. The maximum gray 
value of image analysis department of landscape spatial 
environment design is: 
 



) (
00
pb
pg
n 
 
(8) 
Sparse representation and super-resolution 
reconstruction methods were used for visual 
reorganization of landscape spatial environment design, 

Application of Interactive Genetic Algorithm in Landscape…                                                   Informatica 46 (2022) 365-372     369 
and interactive genetic algorithm was used to realize 
landscape art information fusion perception. The 
information reconstruction model of landscape spatial 
environment design was expressed as follows: 
) , ( ) , ( ) , ( y x y x f y x g    (9) 
𝑓 ( 𝑥 , 𝑦 ) , 𝑔 ( 𝑥 , 𝑦 ) and 𝜀 ( 𝑥 , 𝑦 ) respectively represent 
the original landscape spatial environment image, 
reconstructed image and gray scale image. In summary, 
interactive genetic optimization design of landscape 
design can be carried out. 
 
B. Interactive genetic optimization 
This paper proposes a visual reconstruction 
algorithm for landscape spatial environment design based 
on interactive genetic algorithm [24,25]. Template 
matching method combining block areas landscape space 
environment design of the image characteristics of visual 
reconstruction, based on local feature adaptive feature 
matching method constructs a model of information 
fusion visual landscape space environment, the 
construction of landscape space environment art 
characteristic expression model, under the genetic 
evolutionary optimization, get the garden art landscape 
information fusion expression is: 
n f k g    (10) 
Where  represents convolution operator, carries 
out vector set fusion processing on the collected design 
images of landscape spatial environment, constructs the 
visual feature decomposition model of landscape spatial 
environment, and obtains the best discerning feature 
value of landscape spatial environment vision: 
 

 


    
i
N
j
i c j p s PPM
p
a T c jT iT t p t s
1
0
) ( ) (  (11) 


 
 
j
s j PPM
jT t p d t s ) ( ) ( 
(12) 
Where 𝑇 𝑠 is the optimization iteration width of 
genetic evolution. Under interactive inheritance, the 
block model of garden landscape spatial environment 
design is obtained, as shown in Figure 5. 
 
 
(a): Block matching of left seed points 
 
 
(b): Block matching of right seed points 
Figure 5(a, b): Landscape space environment design 
 
Gray correlation constraint is added to determine the 
final matching point, and the image pixel decomposition 
model is expressed as: 
 
 
   
M
m
m K
k
mk m nk
t v T t s t x
1
) (
1
) ( ) ( ) (   (13) 
In the formula, 𝜔 𝑛𝑘
 is the fuzzy feature component 
of landscape spatial environment vision. Under the 
genetic interactive evolution, the output of landscape 
optimization design is: 
 





 
  
  
2 / , cos
0 , sin sin
2 0 , cos sin
D R R z
R y
R x

   
   
 
(14) 
Among them, 𝜂 represents the landscape spatial 
visual reconstruction function, 𝜑 represents the Angle 
function of landscape spatial environment image visual 
reconstruction, and 𝑅 represents the template matching 
coefficient. 

370     Informatica 46 (2022) 365-372                                                                                                                                    B.  Li et al. 
3 Research results and discussion 
In order to verify the application performance of the 
proposed method in realizing the spatial environment 
design of landscape, simulation experiment analysis was 
conducted. It was assumed that the number of seed points 
in landscape design was 40, the coefficient of feature 
matching was 0.36, and the block size of pixel points was 
200 × 200. The gray scale of landscape design was 
shown in Figure 6. 
 
 
Design Sample 1 
 
Design Sample 2 
Figure 6: Gray scale of landscape design 
 
Taking the image in Figure 6 as the research object, 
the similarity information fusion model is used to carry 
out the perception of landscape art information fusion 
and block region template matching in the process of 
visual reconstruction of landscape spatial environment 
design image to realize the design optimization, and the 
optimized design results are shown in Figure 7. The 
analysis of Figure 7 shows that the proposed method can 
effectively realize the optimal design of landscape space 
environment, with higher image recognition accuracy 
and improved design effect. The output SNR of the 
proposed method is 14.6% higher than that of the 
traditional method. 
 
 
Design Sample 1 
 
Design Sample 2 
Figure 7: Optimization of landscape design 
 
The proposed genetic algorithm-based approach is 
compared with the tradition method in terms of Signal to 
noise ratio as well as accuracy. The outcomes obtained 
are depicted in Figure 8. 
 
 
Figure 8: Comparative analysis of proposed and 
traditional method 
 
The output signal-to-noise ratio is improved by 
14.6% for the proposed approach as compared to the 
tradition approach. The accuracy value obtained using 
0
20
40
60
80
100
120
75%
80%
85%
90%
95%
100%
10 20 30 40 50 60 70 80 90 100
SNR (dB)
Accuracy (%)
Iteration Count
Accuracy for Traditional Approach (%)
Accuracy for the Proposed Approach (%)
SNR for the Proposed Approach(dB)
SNR for Traditional Method(dB)

Application of Interactive Genetic Algorithm in Landscape…                                                   Informatica 46 (2022) 365-372     371 
the proposed method is improved by 7.9% comparative 
to the traditional approach. The outcomes obtained 
depicts the viability of the proposed approach. 
 
4 Conclusion 
This article presents an optimization design method 
of landscape space environment based on interactive 
genetic algorithm. Firstly, the multi-stage feature 
decomposition and grey pixel feature separation of 
landscape spatial environment design image are carried 
out by constructing distributed detection of visual feature 
space and reconstruction model of fuzzy pixel region 
feature fusion. On this basis, the visual feature 
reconstruction model of landscape spatial environment 
design image was established, the fuzzy feature quantity 
of landscape spatial environment design image was 
extracted, and the interactive genetic algorithm method 
was used to realize the landscape information fusion 
perception and visual reconstruction quality evaluation of 
landscape art. The sparse representation and super-
resolution reconstruction methods were used for visual 
reorganization of landscape spatial environment design, 
and interactive genetic algorithm was used to realize 
landscape art information fusion perception and 
landscape design optimization. The simulation results 
show that compared with the traditional method, the 
visual reconstruction quality of landscape spatial 
environment design image processed by this method is 
better along with higher image recognition accuracy of 
96.78%, and the output signal-to-noise ratio is improved 
by 14.6%.  The experimental results prove that the 
introduction of interactive genetic algorithm in landscape 
planning and design can effectively solve the problems 
of multi-level feature decomposition and pixel feature 
separation in the process of landscape design. The 
method proposed achieves better optimization of 
landscape spatial environment structure and has a good 
design effect of landscape spatial environment. 
 
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