The process of robot creation frequently entails combining multiple inflexible parts, subsequently integrating actuators and their control systems. To minimize the computational intricacy, several studies constrain the possible rigid components to a finite set. CHIR-99021 concentration However, this constraint does not only limit the search area, but also obstructs the use of efficient optimization processes. To identify a robot design closer to the global optimal design, it is essential to use a method that examines a more extensive spectrum of robots. A novel method for the efficient discovery of a variety of robot designs is detailed in this article. This method employs a combination of three optimization methods, each with its own distinct set of characteristics. Proximal policy optimization (PPO) or soft actor-critic (SAC) serves as the controller, with the REINFORCE algorithm tasked with ascertaining the dimensions and other numeric parameters of the rigid components. A newly developed methodology determines the quantity and arrangement of the rigid parts and their connections. Physical simulation experiments on walking and manipulation tasks reveal this method to outperform the simple combination of established methods. The experimental data, including video footage and source code, are hosted at the online repository, accessible via https://github.com/r-koike/eagent.

The study of time-varying complex-valued tensor inversion is essential, yet the efficacy of current numerical approaches is disappointing. This investigation aims to find the accurate resolution to the TVCTI using a zeroing neural network (ZNN), a solution-oriented method for tackling time-variable problems. The enhanced ZNN method presented here constitutes the first solution to the TVCTI problem. Drawing from the ZNN design, an error-adaptive dynamic parameter and a novel enhanced segmented exponential signum activation function (ESS-EAF) are first introduced and applied to the ZNN. The TVCTI problem is approached by proposing a parameter-adjustable, dynamically-varying ZNN model (DVPEZNN). The theoretical underpinnings of the DVPEZNN model's convergence and robustness are examined and discussed. The illustrative example evaluates the DVPEZNN model's convergence and robustness against four ZNN models with variable parameters. Across various settings, the DVPEZNN model's convergence and robustness surpass those of the other four ZNN models, as evident from the results. Employing the state solution sequence from the DVPEZNN model for TVCTI resolution, the algorithm merges chaotic systems and DNA coding to develop the chaotic-ZNN-DNA (CZD) image encryption algorithm. This algorithm effectively encrypts and decrypts images.

The deep learning community has recently embraced neural architecture search (NAS) for its impressive capacity to automatically generate deep models. Amongst diverse NAS strategies, evolutionary computation (EC) holds a significant position, owing to its ability to perform gradient-free search. Still, a multitude of current EC-based NAS approaches refine neural network architectures in an entirely discrete way, which results in a restricted capacity for adaptable filter management across different layers. This limitation often stems from reducing choices to a fixed set rather than pursuing a comprehensive search. NAS methods relying on evolutionary computation (EC) are often criticized for their performance evaluation inefficiency, which demands full training for the considerable number of candidate architectures generated. This research proposes a split-level particle swarm optimization (PSO) strategy for resolving the issue of limited flexibility in search results related to the number of filter parameters. Fractional and integer parts of each particle dimension code for layer configurations and a diverse selection of filters, respectively. In addition, a significant reduction in evaluation time is achieved through a novel elite weight inheritance method, leveraging an online updating weight pool. A tailored fitness function incorporating multiple objectives is developed to effectively control the complexity of the search space for candidate architectures. The SLE-NAS, a split-level evolutionary neural architecture search (NAS) method, is computationally efficient and demonstrably surpasses many current state-of-the-art peer methods on three common image classification benchmark datasets while maintaining a lower complexity profile.

The field of graph representation learning research has drawn considerable attention in recent years. Nonetheless, most prior investigations have been focused on the integration of single-layered graph structures. The small body of research focused on learning representations from multilayer structures often operates under the assumption that inter-layer connections are pre-defined; this supposition narrows the possible applications. Generalizing GraphSAGE, we introduce MultiplexSAGE for the purpose of embedding multiplex networks. The results showcase that MultiplexSAGE can reconstruct both intra-layer and inter-layer connectivity, demonstrating its superior performance against other methods. Subsequently, via a thorough experimental investigation, we also illuminate the embedding's performance within both simple and multiplex networks, demonstrating how the graph's density and the randomness of its connections significantly impact the embedding's quality.

Memristors' dynamic plasticity, nanoscale size, and energy efficiency have propelled the growing interest in memristive reservoirs across diverse research fields. PCR Equipment Despite its potential, the deterministic hardware implementation presents significant obstacles for achieving dynamic hardware reservoir adaptation. Evolutionary algorithms currently employed for reservoir design lack the necessary structure for integration into hardware systems. The scalability and practical viability of memristive reservoirs are frequently overlooked. This work develops an evolvable memristive reservoir circuit based on reconfigurable memristive units (RMUs), enabling adaptive evolution for a range of tasks. Crucially, direct evolution of memristor configuration signals avoids the variability that can arise from the memristor devices themselves. We propose, in light of memristive circuit feasibility and expandability, a scalable algorithm for the evolution of this reconfigurable memristive reservoir circuit. The evolved reservoir circuit will be valid under circuit laws and will possess a sparse topology, thus addressing the scalability issue and ensuring circuit practicality throughout the evolutionary process. immune metabolic pathways Our proposed scalable algorithm is subsequently used to evolve reconfigurable memristive reservoir circuits, addressing a wave generation challenge, along with six predictive tasks and one classification task. The efficacy and prominence of our suggested evolvable memristive reservoir circuit are substantiated via experimental procedures.

Epistemic uncertainty and reasoning about uncertainty are effectively modeled through belief functions (BFs), widely applied in information fusion, originating from Shafer's work in the mid-1970s. Their success in practical applications is, however, limited by the substantial computational complexity of the fusion process, especially when the number of focal elements is large. In order to mitigate the complexity of reasoning with basic belief assignments (BBAs), a first method suggests reducing the number of focal elements involved in the fusion, thereby simplifying the initial basic belief assignments. A second method proposes employing a simplified combination rule, potentially compromising the specificity and relevance of the combined result; or, a third combined approach employs both methods together. This article centers on the initial method, introducing a novel BBA granulation approach, drawing inspiration from the community clustering of graph network nodes. The subject of this article is a novel, efficient multigranular belief fusion (MGBF) technique. Within the graph's structure, focal elements are represented by nodes, the distances between which are indicators of local community relationships for focal elements. Following this, the nodes within the decision-making community are carefully selected, and this allows for the efficient amalgamation of the derived multi-granular sources of evidence. The graph-based MGBF is further examined for its effectiveness in integrating the results from convolutional neural networks enhanced by attention mechanisms (CNN + Attention) in the context of human activity recognition (HAR). The experimental results, using genuine datasets, definitively validate the compelling appeal and workability of our proposed approach, far exceeding traditional BF fusion techniques.

The timestamp is integral to temporal knowledge graph completion, an advancement over static knowledge graph completion (SKGC). The existing TKGC methods generally operate by converting the original quadruplet to a triplet format, incorporating the timestamp into the entity or relationship, and subsequently using SKGC methods to infer the missing item. Nevertheless, this unifying operation significantly diminishes the potential for conveying temporal nuances, neglecting the loss of meaning resulting from entities, relations, and timestamps being situated in distinct spaces. In this article, we propose a novel approach to TKGC, the Quadruplet Distributor Network (QDN). It models entity, relation, and timestamp embeddings distinctly in their respective spaces to represent all semantics completely. The QD then is employed to support information distribution and aggregation across these elements. A novel quadruplet-specific decoder is instrumental in integrating the interaction of entities, relations, and timestamps, thus extending the third-order tensor to meet the TKGC criterion as a fourth-order tensor. Of equal importance, we introduce a novel temporal regularization approach that mandates a smoothness constraint on temporal embeddings. Evaluative trials highlight the superior performance of the introduced method over the prevailing TKGC standards. The source code repository for this article regarding Temporal Knowledge Graph Completion is located at https//github.com/QDN.git.