# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 4 The Interacting Dimensions of this Complex Socio-Technical Ecosystem
### Nature-Inspired Computational Approaches, Neurosciences, and Neural Computing  
Nature-inspired computational approaches, also known as evolution strategies, are regarded as a sub-class of direct search algorithms and optimization methods. They operate by mutation, recombination, and selection, which they apply to a population that contains candidate solutions. They evolve iteratively to achieve better and better solutions, hence they are said to belong to a sub-class of evolutionary algorithms [56]. In computer science, these strategies are used as optimization techniques belonging to a general class known as evolutionary computation methods or as artificial evolution methods. These strategies operate by using the natural problem-dependent representations. These are primarily mutation and selection and search operators that are applied in an iteration loop. Each iteration of a loop is referred to as a generation, and each sequence of generations is continued until pre-defined criteria--termination criteria--are met. It is worth noting that the RL theory provides a normative account of concepts deeply rooted in the psychological as well as the neuro-scientific perspectives of animal behavior. An example of this is based on the behavior of algorithm agents when optimizing the control of their environment. In addition, neural computing has been identified as a perfect alternative computing technique of the post-Moore's Law era, even though much of the research attention has been directed to specialized applications [57]. The RL theory, through incorporating neural computing, gave rise to the development of the DRL strategy, as well as other AI strategies with usefulness in situations that are approaching real-world complexity, such as in robot navigation. In DRL, the computing agents are developed through the training DNNs in order to tackle the difficult task of deriving efficient environmental representations using high-dimensional sensory inputs, which they use in generalizing previous experiences to create new ones. Some deep architectures of DRL, such as the deep Q-learning networks (DQNs), even go to the extent of storing their past results and experience for later replay when encountering recurring problems.  
**Example 2**.: _Group behavior has had manifestations in wireless communications, with solutions from nature-inspired algorithms, such as (i) genetic algorithms, (ii) particle-swarm optimization, as well as (iii) DNNs, where cognitive radios (CRs) make decisions that not only control their fate but also the fate of others. In a similar way to human society, CR society is also a hierarchical society that has attributes that are similar to those possessed by human beings. One of their outstanding human-like attributes is self-organization [58]. In addition, similar to the central government of a country with specific reference to its system of law enforcement, a CR society also has basic etiquettes that all nodes in the CRN need to comply with. In both cases, more especially in the CRN, the main objective of having these etiquettes is to minimize unwarranted as well as excessive interference, i.e., listen before transmitting. In addition, during channel access, these etiquettes promote efficient channel access, e.g., one must never hold a channel unnecessarily if there are no packets to transmit. However, some shortcomings in CRNs have been studied using micro-economic game-theoretical analysis and have been addressed using psychological models derived directly from human behavior, e.g., game theory and RL. Therefore, a CRN can mimic a human society since it may behave rationally when it is competing and cooperating for resources in order to achieve survival as well as social efficiency, the same way as in the human society [52]. However, it must be noted that despite all these laws of behavior, irrational behavior due to Byzantine failures may also be exhibited by SUs [59], leading to security vulnerabilities as well as loss of spectral efficiency and instability in the CRN._