Community Detection (CD) in weighted social networks is a highly active research field, celebrated for its profound practical implications across a multitude of disciplines. Genetic algorithms (GAs) are frequently explored to tackle CD problems, leveraging their capability to navigate the extensive discrete search space effectively. Throughout the evolutionary process, genetic operators such as crossover and mutation assume pivotal roles in effectively exploring the vast solution space. Nonetheless, prevailing GA-based approaches often ignore crucial topology information, particularly information regarding edge weights, resulting in compromised algorithm performance. In light of this, this paper introduces Edge Information-based GA (EIGA) to effectively solve CD problems in weighted networks. This is achieved specifically through the innovative designs of edgeweight-aware crossover and mutation operators. These novel edge-weight-aware operators improve the extraction of meaningful community structures, advancing knowledge discovery from social networks. Empirical findings demonstrate the superior performance of EIGA over numerous state-of-the-art algorithms across various real-world and synthetic benchmark networks.

Community detection, a vital technology for real-world applications, uncovers cohesive node groups (communities) by leveraging both topological and attribute similarities in social networks. However, existing Graph Convolutional Networks (GCNs) trained to maximize modularity often converge to suboptimal solutions. Additionally, directly using human-labeled communities for training can undermine topological cohesiveness by grouping disconnected nodes based solely on node attributes. We address these issues by proposing a novel Topological and Attributive Similarity-based Community detection (TAS-Com) method. TAS-Com introduces a novel loss function that exploits the highly effective and scalable Leiden algorithm to detect community structures with global optimal modularity. Leiden is further utilized to refine human-labeled communities to ensure connectivity within each community, enabling TAS-Com to detect community structures with desirable trade-offs between modularity and compliance with human labels. Experimental results on multiple benchmark networks confirm that TAS-Com can significantly outperform several state-of-the-art algorithms.

Long Short Term Memory (LSTM) network is an effective and popular deep learning model for processing complex sequential data. However, using a single LSTM network does not always achieve reliable performance with the increasing complexity of machine learning tasks. An important approach to improve the performance of LSTM networks is to learn an ensemble of these networks. This paper proposes a new multi-objective evolutionary algorithm to design ensembles of LSTMs. We use NSGA-II to evolve accurate and diverse LSTMs as the building blocks for creating effective ensembles. An integrated ensemble genetic algorithm further evolves the best-performing ensembles from the LSTMs created in each generation of NSGA-II in an efficient manner, allowing all LSTMs evolved by NSGA-II to be explicitly considered for building ensembles. The results show that the proposed approach significantly improves the efficiency and effectiveness over many state-of-the-art machine learning models on various classification and regression tasks.

The long short term memory (LSTM) network is a popular deep learning model with a wide range of applications. Skip connection is a promising and important architectural innovation that can noticeably improve the performance of LSTM networks on complex machine learning tasks with longterm temporal/spatial dependencies. However, it is difficult to manually design skip connections in deep LSTM networks. For example, iterative analysis of skip connections is impractical and has scalability challenges. This paper proposes a new approach based on genetic algorithm (GA) and differentiable architecture search to automatically design LSTM networks with suitable skip connections. To allow an LSTM network and its skip connections to be designed jointly, we relax the search space of skip connections to be continuous. Consequently, LSTM network architectures with appropriate skip connections can be optimized directly through gradient-based network training. The cost of designing skip connections can also be maintained at a low level. Experiment results obtained from various classification and regression tasks show that the proposed algorithm excels in evolving LSTM networks that can significantly outperform several state-of-the-art models.

Long Short Term Memory (LSTM) is a special kind of Recurrent Neural Networks popularly used in various applications. However, using a single LSTM is often not enough to attain reliable performance on complicated machine learning tasks. This is because LSTM is sensitive to the specifics of the training data. Ensemble learning is a promising approach to improve the performance of LSTMs on complicated tasks. However, it is difficult to design an ensemble of LSTMs. LSTMs that constitute the ensemble should be both accurate and diverse. This paper proposes a new two-phase evolutionary algorithm to design ensembles. The first phase is to evolve best performing LSTMs automatically. A connection weight inheritance approach is used in the first phase to improve the effectiveness and efficiency of the evolutionary process. The second phase is to design ensembles by choosing suitable LSTMs without fixing the ensemble size in advance. We use bagging to train the selected LSTMs to build the ensemble to achieve good diversity among the LSTMs. The proposed approach is evaluated on various classification tasks. The results show the effectiveness of the proposed approach and its significant improvement in performance over many state-of-the-art machine learning models. The results also show the efficiency of the proposed approach in comparison with the baseline algorithm.

Deep Recurrent Neural Network (DRNN) is an effective deep learning method with a wide variety of applications. Manually designing the architecture of a DRNN for any specific task requires expert knowledge and the optimal DRNN architecture can vary substantially for different tasks. This paper focuses on developing an algorithm to automatically evolve task-specific DRNN architectures by using a Genetic Algorithm (GA). A variable-length encoding strategy is developed to represent DRNNs of different depths because it is not possible to determine the required depth of a DRNN in advance. Activation functions play an important role in the performance of DRNNs and must be carefully used in these networks. Driven by this understanding, knowledge-driven crossover and mutation operators will be proposed to carefully control the use of activation functions in GA in order for the algorithm to evolve best performing DRNNs. Our algorithm focuses particularly on evolving DRNN architectures that use Long Short Term Memory (LSTM) units. As a leading type of DRNN, LSTM-based DRNN can effectively handle long-term dependencies, achieving cutting-edge performance while processing various sequential data. Three different types of publicly available benchmark datasets for both classification and regression tasks have been considered in our experiments. The obtained results show that the proposed variable-length GA can evolve DRNN architectures that significantly outperform many state-of-the-art systems on most of the datasets.

Recurrent Neural Networks (RNNs) are a major class of artificial neural networks and one of the most effective deep learning models for human activity recognition (HAR). However designing the architecture of an RNN together with suitable hyper-parameters for any learning task can be very time-consuming and requires expert domain knowledge. This paper focuses on exploring a Genetic Algorithm (GA) based method to automatically design suitable architectures of Long Short Term Memory (LSTM)-based RNNs in a fully automated manner. An encoding strategy is proposed to encode the architectures and hyper-parameters of LSTM, for the sake of easy operation by GA. As a variant of RNN, LSTM is selected for this work due to its special feature to handle long-term dependencies. For verification and evaluation, three real-world benchmark datasets are used. We study three models of operation for the evolved deep LSTM-RNN, i.e., unidirectional, bidirectional and cascaded. The results of our experiments show that the RNN architectures automatically designed by our GA method can outperform state-of-the-art RNN systems and machine learning systems for HAR.

Soft computing techniques including fuzzy logic have been successfully applied to Wireless Body Area Networks (WBANs). However, most of the existing research works rely on manual design of the Fuzzy Logic Controller (FLC). To address this issue, in this paper, we propose to use Evolutionary Computation (EC) techniques to automate the design of FLCs for cross layer medium access control in WBANs. With the goal of improving network reliability while keeping the communication delay at a low level, we have particularly experimented on three different coding schemes. The influence of fitness functions has also been examined carefully in order to achieve a good balance between reliability and performance. We have also evaluated the effectiveness of two widely used evolutionary algorithms. Particularly, Particle Swarm Optimisation (PSO) is shown to be more effective than Differential Evolution (DE) for our design problem. Moreover, the FLC designed by our approach is also shown to outperform some related algorithms as well as the IEEE 802.15.4 standard.

Over the past decade, advances in electronics, computer science, and wireless technologies have brought Wireless Body Area Network (WBAN) into many interesting applications. Particularly for the healthcare application, reliability is considered as a very important aspect for WBAN. Being the main focus of this paper, we aim at improving reliability by reducing the collision rate and increasing the packet delivery ratio. We also strive to enhance the performance in terms of throughput in the WBAN while maintaining message latency at a reasonable level. In an effort to achieve these goals, we introduce a new cross-layer fuzzy logic based backoff mechanism. Through this method, instead of relying merely on Medium Access Control (MAC), information from physical and application layers will be exploited as well. Moreover, since independent decision making is supported by each sensor without relying on any coordinating devices, communication in the WBAN becomes very flexible. Specifically, rather than determining Backoff Exponent (BE) in IEEE 802.15.4 through a blind try-and-error process, the proposed fuzzy logic system determines the BE by considering both the current channel condition and application requirements. This feature gives the proposed system a higher level of adaptability. The simulation results clearly show noticeable improvement in reliability and performance without significantly increasing the message latency.