The recent advancements in small-size inference models facilitated AI
deployment on the edge. However, the limited resource nature of edge devices
poses new challenges especially for real-time applications. Deploying multiple
inference models (or a single tunable model) varying in size and therefore
accuracy and power consumption, in addition to an edge server inference model,
can offer a dynamic system in which the allocation of inference models to
inference jobs is performed according to the current resource conditions.
Therefore, in this work, we tackle the problem of selectively allocating
inference models to jobs or offloading them to the edge server to maximize
inference accuracy under time and energy constraints. This problem is shown to
be an instance of the unbounded multidimensional knapsack problem which is
considered a strongly NP-hard problem. We propose a lightweight hybrid genetic
algorithm (LGSTO) to solve this problem. We introduce a termination condition
and neighborhood exploration techniques for faster evolution of populations. We
compare LGSTO with the Naive and Dynamic programming solutions. In addition to
classic genetic algorithms using different reproduction methods including
NSGA-II, and finally we compare to other evolutionary methods such as Particle
swarm optimization (PSO) and Ant colony optimization (ACO). Experiment results
show that LGSTO performed 3 times faster than the fastest comparable schemes
while producing schedules with higher average accuracy.

通过提出轻量级混合遗传算法来解决小型推理模型在边缘设备上动态分配推理任务以最大化推理准确度的问题，并与其他演化方法进行比较。

最大推理准确性和能效的选择性任务卸载用于实时物联网感知系统

Selective Task offloading for Maximum Inference Accuracy and Energy  efficient Real-Time IoT Sensing Systems

Virtual network embedding is one of the key problems of network
virtualization. Since virtual network mapping is an NP-hard problem, a lot of
research has focused on the evolutionary algorithm's masterpiece genetic
algorithm. However, the parameter setting in the traditional method is too
dependent on experience, and its low flexibility makes it unable to adapt to
increasingly complex network environments. In addition, link-mapping strategies
that do not consider load balancing can easily cause link blocking in
high-traffic environments. In the IoT environment involving medical, disaster
relief, life support and other equipment, network performance and stability are
particularly important. Therefore, how to provide a more flexible virtual
network mapping service in a heterogeneous network environment with large
traffic is an urgent problem. Aiming at this problem, a virtual network mapping
strategy based on hybrid genetic algorithm is proposed. This strategy uses a
dynamically calculated cross-probability and pheromone-based mutation gene
selection strategy to improve the flexibility of the algorithm. In addition, a
weight update mechanism based on load balancing is introduced to reduce the
probability of mapping failure while balancing the load. Simulation results
show that the proposed method performs well in a number of performance metrics
including mapping average quotation, link load balancing, mapping cost-benefit
ratio, acceptance rate and running time.

在 IoT 环境下，为了在网络负载大且性能需求高的情况下提供更灵活的虚拟网络映射服务，提出了一种基于混合遗传算法的虚拟网络映射策略，包括动态计算交叉概率和基于信息素的变异基因选择策略，以及基于负载均衡的权重更新机制。仿真结果表明，该方法在多项性能指标方面表现良好。

基于负载均衡的物联网多域 VNE 算法

A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks

Worldwide research shows that millions of lives lost per year because of
heart disease. The healthcare sector produces massive volumes of data on heart
disease that are sadly not used to locate secret knowledge for successful
decision making. One of the most important aspects at this moment is detecting
heart disease at an early stage. Researchers have applied distinct techniques
to the UCI Machine Learning heart disease dataset. Many researchers have tried
to apply some complex techniques to this dataset, where detailed studies are
still missing. In this paper, Principal Component Analysis (PCA) has been used
to reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means
used for final clustering. Typically, the k-means method is using for
clustering the data. This type of clustering can get stuck in the local optima
because this method is heuristic. We used the Hybrid Genetic Algorithm (HGA)
for data clustering to avoid this problem. Our proposed method can predict
early heart disease with an accuracy of 94.06%.

通过使用 PCA 降维和混合遗传算法 (HGA) 进行数据聚类，本论文提出的方法可以以 94.06％的准确度预测早期心脏病，主要关键词为心脏病，UCI 机器学习，主成分分析，混合遗传算法和早期检测。