Research on Genetic Algorithm Model for Organizational Capability
Evolution Based on CAS Theory
Tianyu Xue
Bailie Vocational College Zhangye, Gansu, China
Keywords: Statistical Theory, CAS Theory, College Students, Psychological, Model Studies.
Abstract: Genetic algorithm model research plays a crucial role in the evolution of enterprise organizational capabilities,
however, the traditional ant colony algorithm has certain limitations for solving the problem of model
research, and its effect is not ideal. This paper explores the evolutionary mechanism of organizational
capability under the theory of Complex Adaptive System (CAS), and introduces a genetic algorithm model to
simulate and optimize this process. By analyzing the application of genetic algorithms in the evolution of
organizational capabilities, it aims to provide a persuasive framework for enterprises to promote their
adaptability and competitiveness. The simulation results based on MATLAB show that under certain
evaluation criteria, the genetic algorithm model research scheme based on CAS theory shows obvious
advantages in the accuracy of genetic algorithm model research and the processing time of influencing factors
of genetic algorithm model research, which can achieve ideal results comprised with the traditional ant colony
algorithm.
1 INTRODUCTION
Genetic algorithm model research plays an important
role in the evolution of enterprise organizational
capabilities (Ji and Huang, et al. 2023), which can
realize the precise positioning and real-time control
of genetic algorithm model research (Ren and Wu, et
al. 2021). However, the traditional genetic algorithm
model research scheme has the of poor accuracy (Ji
and Chen, 2023), which has an adverse impact on the
research effect of the genetic algorithm model (Gu,
2022). In an ever-changing market environment, the
capabilities of business organizations need to evolve
to adapt to external challenges (Zeng and Zhu, et al.
2021). Complex Adaptive Systems (CAS) theory
provides a theoretical basis for understanding how
firms adapt to environmental changes as an organic
whole (Ren and Wu, et al. 2021). The CAS theory
views the enterprise as a system of interacting
individuals who are able to learn, evolve, and adapt
to each other. In such a system, the genetic algorithm
model can be used as an effective tool to simulate the
evolution process of enterprise capabilities (Xue and
Lin, et al. 2021).
2 RELATED CONCEPTS
2.1 Mathematical Description of the
CAS Theory
Genetic algorithms (GAs) are search heuristics that
mimic natural selection and genetics. It simulates the
genetics, mutations, selection, and crossover of
biological evolutionary processes to find the optimal
solution or the most adaptable solution (Zhou, 2023).
In the evolution of organizational capabilities, genetic
algorithms can help enterprises find the most
effective capability allocation among many possible
capability combinations.
lim( ) lim max( 2)
iij ij ij
xx
yt y t
→∞ →∞
⋅= ≥ ÷Κ
(1
)
The judgment of outliers is shown in Equation (2).
2
1
max( ) ( 2 ) ( 4)
2
ij ij ij ij
ttt t=∂ + ⋅ +
M
(2
)
526
Xue, T.
Research on Genetic Algorithm Model for Organizational Capability Evolution Based on CAS Theory.
DOI: 10.5220/0013548300004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 526-531
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
First of all, we need to define the "genes" of the
organization's capabilities. These can be a business's
key business processes, management practices,
technological innovations, or anything that can
impact the performance of the business (Cheng and
Shao, et al. 2023). The different combinations of
these genes represent the different capabilities of the
enterprise.
()
!
() 2 7
!!
iii
n
Fd t y
rnr
ξ
=⋅→⋅
−
(3
)
2.2 Selection of Genetic Algorithm
Model Research Scheme
Next, the genetic algorithm simulates the evolution of
enterprise capabilities through the following steps:
Initialize Populations – Create a random set of
enterprise capability configurations as the initial
populations.
()= ( )
ii i i
dy
gt x z Fd w
dx
⋅−ΚΦ
∏
(4)
Assess Fitness – Assess the fitness or
performance of each capability configuration based
on the market environment and internal conditions of
the enterprise.
lim ( ) ( ) max( )
ii ij
x
y
gt Fd t
x
→∞
Δ
+≤
Δ
(5)
Selection – Select the best competency profile
based on fitness scores as the foundation for the next
generation.
() ( ) ( 4)
ii ij
gt Fd mean t+↔ +
(6
)
2.3 Analysis of the Genetic Algorithm
Model Research Scheme
Crossover & Mutation – Cross and recombine
selected configurations, as well as random variations,
to generate new diversity. Repeat – Iterate over and
over again until you find the optimal enterprise
capability configuration or reach a preset number of
iterations.
2
() ( )
()
(4)
ii
i
ij
gt Fd
No t
mean t u v
+
∂Ω
=
+∂∂
(7
)
In this way, genetic algorithms can not only help
companies identify and develop the
most appropriate
capability configuration for the current environment,
but also be able to anticipate possible future changes
and prepare in advance (Liu and Ma, et al. 2023). This
forward-looking capability adjustment is the key for
enterprises to maintain a leading position in the fierce
market competition.
() [ () ( )]
iii
Z
ht gt Fd=+
(8
)
As a powerful optimization tool, the application
of genetic algorithm in the evolution of enterprise
organizational capability based on CAS theory
provides a methodology for enterprises to
dynamically adapt to environmental changes (Cheng
and Liu, et al. 2023). By simulating the process of
biological evolution, companies can continuously
explore and optimize their own capabilities to gain an
advantage over the competition.
min[ ( ) ( )]
( ) 100%
() ( )
ii
i
ii
gt Fd
accur t
gt Fd
+
=×
+
R
(9
)
The application of genetic algorithms not only
enhances the adaptability of enterprises, but also
promotes their continuous innovation and
development (Xie and Zhao, et al. 2021). With the
increasing complexity of the business environment,
mastering advanced tools such as genetic algorithms
has become an indispensable core competitiveness
for enterprises.
min[ ( ) ( )]
()
() ( )
ii
i
ii
gt Fd
accur t
gt Fd
+
=
+
(10
)
Within the theoretical framework of Complex
Adaptive Systems (CAS), a company is seen as an
organism that is constantly evolving, learning, and
adapting to its environment. In such a system, the
evolution of organizational capabilities is the key to
the survival and development of enterprises. Genetic
algorithms (GA), as an optimization technology that
simulates the evolutionary process in nature, have
been proven to have unique advantages in promoting
the evolution of organizational capabilities.
Research on Genetic Algorithm Model for Organizational Capability Evolution Based on CAS Theory
527
3 OPTIMIZATION STRATEGIES
FOR GENETIC ALGORITHM
MODEL RESEARCH
In this paper, we will discuss how genetic algorithm
can promote the evolution of enterprise
organizational capabilities under CAS theory, and
analyze its application and challenges in practical
operation. Genetic algorithms enable firms to
discover optimal or near-optimal solutions in their set
of organizational capabilities by simulating variation,
crossover, and selection mechanisms in the process of
natural selection.
3.1 Introduction to the Research of
Genetic Algorithm Model
From the perspective of CAS theory, the evolution of
enterprise organizational capabilities is not only an
optimal allocation of existing resources, but also a
dynamic process involving innovation, learning, and
adaptation. Genetic algorithms provide a framework
that enables companies to find the most adaptable
combination of capabilities in an ever-changing
market environment in Table 1.
Table I. Genetic algorithm model research requirements
Scope of
application
Grade Accuracy Genetic
algorithm
model
research
Small business I 91.39 90.41
II 88.81 88.14
Medium-sized
b
usinesses
I 88.90 86.69
II 92.59 89.12
Large I 90.88 90.29
II 91.62 88.93
The genetic algorithm model research process in
Table 1 is shown in Figure 1.
CAS theoryAnalyse
Pattern
Statistical
theory
Consider
University
Psychology
Figure 1: The analysis process of a genetic algorithm model
study
First of all, the core of genetic algorithms lies in
their ability to handle complex search spaces, which
means being able to find the best combination of
multi-dimensional organizational capabilities. For
example, enterprises need to continuously optimize in
many aspects such as technological innovation,
market development, and human resource
management. Genetic algorithms are able to take
these dimensions into account and progressively
approximate the optimal solution through an iterative
process.
3.2 Research on Genetic Algorithm
Models
Second, genetic algorithms emphasize population-
level evolution rather than individual evolution. In the
evolution of organizational capabilities, this means
that the entire organization needs to evolve as a
collective, rather than relying on the optimization of
individual departments or individuals. The
accumulation and inheritance of collective wisdom is
the key to maintaining the competitiveness of
enterprises in the fierce market competition.as shown
in Table 2.
Table 2: The overall situation of the genetic algorithm
model research scheme
Category Random
data
Reliability Analysis
rate
Small
b
usiness
89.55 89.66 91.77
Medium-
sized
b
usinesses
87.95 91.54 91.65
Lar
g
e 90.60 89.82 91.70
Mean 91.73 92.95 90.93
X6 91.90 88.05 92.17
P=1.249
3.3 Genetic Algorithm Model Research
and Stability
Furthermore, the mutation manipulation in genetic
algorithms provides innovative possibilities for the
evolution of organizational capabilities. in Figure 2.
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528
Figure 2: Study of genetic algorithm models for different
algorithms
In practice, enterprises can simulate the mutation
process by introducing new workflows, management
methods or technological innovations, so as to jump
out of the local optimal and find a new evolutionary
path.
Table 3: Comparison of the research accuracy of genetic
algorithm models of different methods
Algorith
m
Surve
y data
Genetic
calculation
s Law
model
research
Magnitud
e of
change
Error
CAS
Theor
y
90.46 92.52 87.30 90.4
7
Ant
colony
algorith
m
90.39 91.02 89.06 89.0
6
P 92.35 90.65 91.71 88.7
1
However, genetic algorithms also face challenges
in practical applications. The first is the selection of
parameters, such as the rate of variation, crossover
rate, and selection strategy, which directly affect the
efficiency of the algorithm and the quality of the final
result. The second is how to combine the abstract
model of genetic algorithm with the actual operation
of the enterprise, which requires a deep understanding
of the business process and management mechanism.
Finally, genetic algorithms require a sufficient
number of iterations to ensure that the solution space
is fully explored, which is a test of time and resource
investment.and the specific results can be referred to
Figure 3. These professional analysis data further
verify the superiority of CAS theory in the study of
genetic algorithm models.
Figure 3: Research on the genetic algorithm model of CAS
theory
In summary, genetic algorithm, as an optimization
tool, plays an important role in the evolution of
enterprise organizational capabilities under the
guidance of CAS theory. By simulating the
evolutionary mechanisms of nature, genetic
algorithms can help companies find the most effective
combination of organizational capabilities in a
complex and volatile market environment. Despite
some challenges, genetic algorithms will undoubtedly
provide strong support for the continued growth and
adaptability of enterprises as long as they are properly
designed and applied.
3.4 Rationality of Genetic Algorithm
Model Research
In order to verify the accuracy of the CAS theory, the
genetic algorithm model research scheme is
comprised with the ant colony algorithm, and the
genetic algorithm model research scheme is shown in
Figure 4.
Figure 4: Study of genetic algorithm models for different
algorithms
Research on Genetic Algorithm Model for Organizational Capability Evolution Based on CAS Theory
529
In complexity science, the Complex Adaptive
System (CAS) theory reveals how systems in a
constantly changing environment adapt and evolve
through self-adjustment. Genetic Algorithms (GA) is
an intelligent search optimization method that mimics
the process of biological evolution. The combination
of the two shows great potential to improve the
organizational capacity of enterprises, like the double
helix of DNA, intertwined with each other, and
together promote the adaptability and innovation of
enterprises to a new level.
3.5 Effectiveness of Genetic Algorithm
Model Research
First, let's examine the core elements of CAS theory
– adaptability, nonlinearity, and emergence. As a
typical complex adaptation system, the structure and
function of an enterprise organization are constantly
adjusted according to changes in the external
environment. This bottom-up, self-organizing
structure gives businesses the flexibility and
resilience they need in the face of uncertainty. in
Figure 5 shows below.
Figure 5: Research on genetic algorithm models for
different algorithms
At the same time, the nonlinear characteristics
suggest that small input changes can trigger
unpredictable large outcomes, emphasizing the
development characteristics of firms that are sensitive
to initial conditions and dependent paths. The
emergent phenomenon explains how new macro-
level attributes or functions arise through interactions
between individuals, which is the source of
innovation in Table 4.
Table 4. Comparison of the effectiveness of genetic
algorithm model research of different methods
Algorithm Survey
data
Genetic
algorithm
model
research
Magnitude
of change
Error
CAS
Theor
y
89.70 87.52 91.35 87.54
Ant
colony
al
g
orith
m
92.58 88.94 88.58 93.26
P 88.05 93.03 90.70 91.09
It is in this theoretical context that genetic
algorithms come into play. As a computational model
of natural selection, it draws on the mechanisms of
crossover, variation, and selection in biological
genetics to solve optimization problems. In the
construction of enterprise organizational capabilities,
we can regard various business strategies and
management models as different "genes", which
finally form the most suitable corporate strategy for
the current environment by simulating the genetic
process in nature, through a series of iterations and
screenings. In this process, the principle of "survival
of the fittest" is applied to business management
practices in order to achieve the optimal
organizational state. Figure 6 shows below.
Figure 6: Research on the genetic algorithm model of CAS
theory
Combining CAS theory and genetic algorithms,
we realize that the improvement of organizational
capabilities is not an overnight result, but a
continuous evolutionary process. In this process,
various factors within the enterprise – such as
employee skills, management processes, corporate
culture, etc. – are like various genes in the genetic
material, they interact with each other, influence each
other, and promote the evolution and maturity of the
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entire organization through continuous trial and error,
learning, and adaptation.
In practice, the use of genetic algorithms for
organizational capacity optimization can be
manifested in a variety of ways. For example, in the
development of new products, companies can
efficiently find the product solution that best meets
the needs of the market by simulating the
combination of different product designs (gene
combinations), market feedback (natural selection),
and design improvements (variation). Or in human
resource management, by simulating the "genetic"
process of employee training programs, the training
model that best enhances team performance is
selected.
4 CONCLUSIONS
In addition, the combination of CAS theory and
genetic algorithm also provides a new perspective for
enterprises to understand internal conflicts and
contradictions. These seemingly negative factors may
actually be important drivers of enterprise evolution.
Just as mutations in biological evolution can
sometimes lead to greater adaptability, some "non-
traditional" thinking and approaches in a business can
be the key to breaking new ground.
In conclusion, the combination of CAS theory and
genetic algorithm provides us with a powerful set of
tools and frameworks to understand and guide the
construction of organizational capabilities. This is not
only a revolution in enterprise management theory,
but also a useful attempt to practice business. When
we broaden our vision to the global level of complex
systems, we can more clearly capture the context of
organizational development, so as to take advantage
of the fierce market competition.
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