# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 3 Routing on Wireless Sensor Networks
### Cluster Properties
#### 3.6.2 Genetic Algorithms (GAs)  
A more efficient approach would involve using a GA to calculate the routing topology of the WSN again in the present while taking each node's energy level and distance from other nodes into account. Fitness tests are run on the new structures to select the fittest persons in order to enhance the GA models. This optimization technique benefits from the information from random searches and routes. GAs have demonstrated their ability to adapt to changing conditions, making them deployable. The six basic elements of the GA global scheme are the preliminary set of outlines, chromosomal ciphering task, gene operator, assessment function, choice method, and final set of configurations.  
The \"genetic algorithm to extend the lifespan of sensor networks at two levels (GABEC)\" of [PERSON] et al. is proposed in [17]. The GABEC algorithm is proposed by [PERSON] et al. in [18]. The protocol has two phases: the first is the configuration phase, and the second is the steady-state phase. Based on energy entropy, [PERSON] et al. [19] propose a genetic process for multipath routing in WSNs (GAEMW). In order to boost lifetime efficiency and WSN steadiness, the authors of [20], [PERSON] et al., disseminated an additional adaptive energy-efficacy clustering hierarchy protocol (GAEEP) constructed in a genetic process. The protocol's goal is to rise the valuable lifetime of WSNs by determining the best placement for CHs and the minimum possible number of them to use, all while decreasing the energy requirement of separate sensor nodes. The goal of the research presented by [PERSON] et al. in [21] was to extend the life of networks and use less energy. Here, we use the constraints of \"BS distance, data transfer, cluster distance, and energy level\" [21]. We find that LEACH, HCR-1, and HCR-2 all perform at or near the same level. According to the outcomes, optimizing the fraction of the network that is actively processing data is more effective than using more algorithms.  
In order to attain optimum thresholding for cluster construction, the authors [PERSON] et al. suggest a GA-based adaptive clustering algorithm. LEACH-GA is a GA-based enhancement of the original LEACH [22]. Moreover, the LEACH-GA technique outperforms DT, LEACH, and MTE in terms of network lifetime, and its use of the finest possibility accepts optimum energy-efficient clustering. This is in comparison to the LEACH configuration and static state. The suggested algorithm calculates the best clustering threshold. The findings demonstrate that, in comparison to leech-GA leeches, the network lifetime can be better quality by growing the number of live nodes per cycle. Complex systems known as Artificial Neural Networks (ANNs) use components referred to as neurons or nerve cells to carry out parallel or distributed processing. Synapses, which are weighted connections, are used for communication between neurons. ANNs are computational processes that can generate principal multifaceted mapping involving different parameters by means of a supervised learning method in order to finish the categorization of data using an unsupervised manner.