The properties of
an object define its attributes and operational characteristics, which in turn
influence default values and system behavior.
1- Each object
can incorporate specific Genetic Principles and unique inheritance patterns.
2-Genetic
Principles represent comprehensive techniques for problem-solving and
optimization.
3-These
principles leverage the natural selection process of Genetic Algorithms and
pattern-matching mechanisms within an object.
4-System Owners
can develop Genetic Algorithms within the Input Framework to enhance system
performance.
5-This approach
ensures harmonic balance within the designated system platform.
Genetic
Algorithms provide infrastructure support for data resources within the input
framework, integrating internal and external resources on the system platform.
Encapsulated Genetic Algorithms optimize resource allocation and facilitate
sustainable process operations.
A Genetic
Algorithm builds and instantiates input parameters within objects, optimizing
all resources through an Information Processing System. Furthermore, it
establishes standards and guidelines to ensure high-quality outputs.
Genetic
Algorithms also extend system platform capabilities to meet operational and
functional requirements. For example, Input 1 with an encapsulated Genetic
Algorithm generates Output 1, following a Genetic Algorithm pattern. By
analogy, similarities can be identified between the properties of Input 1 and
Output 1. The Genetic Algorithm within the Input 1 Framework optimizes all
resources and system performance while setting realistic goals. As a result,
Output 1 achieves broader coverage of complex parameters than an
"Unconventional Algorithm" within the system platform. Ultimately,
outcome mapping for the encapsulated Genetic Algorithm forms a Closed-loop
Model Structure. (Fig 1)
In contrast,
System Owners develop "Unconventional Algorithms" to address complex
scenarios in the rationalization process. Input 2, with an encapsulated
Unconventional Algorithm, processes the Output 2 framework through the
Information Processing System. Transparent analogical patterns between Input 2
and Output 2 help identify allocated Unconventional Algorithms.
Unconventional
Algorithms primarily focus on managing complex instance parameters within the
rationalization process. Unlike Genetic Algorithms, Individualistic Algorithms
struggle to effectively respond to internal and external resources. They also
have difficulty perceiving and defining functional and operational strategy
requirements. Consequently, outcome mapping for encapsulated Unconventional
Algorithms results in a partial Open-loop Structure Model.
Due to parameter
complexity, multiple suboptimizations may be required throughout the system's
evolutionary path to enhance performance. (Fig 1)
