Fan Cheng

Evolutionary algorithms (EAs) have demonstrated excellent performance in many fields, yet their theoretical research remains relatively underdeveloped. As an important combinatorial optimization problem, subset selection provides a critical research avenue to advance the theoretical study of EAs. However, most existing studies focus on solving various variants of subset selection problems, lacking in-depth exploration of more knowledge used in evolutionary operators. This makes it difficult to design EAs that are both more effective and theoretically guaranteed. Therefore, we propose a theoretically guaranteed EA with dual-knowledge integration for subset selection (DKESS). The algorithm integrates two key types of knowledge: the prior fitness contribution knowledge and the population historical statistics knowledge, to comprehensively evaluate the importance of variables and guide the optimization process, thereby enhancing optimization performance with theoretical guarantees. Specifically, we construct a dual-knowledge integration strategy for assessing variable importance, based on which two novel operators are designed: the dual-knowledge guided crossover operator (DKC) and the dual-knowledge guided mutation operator (DKM). These two operators can be used to effectively identify and select high-value variables during the optimization process, thus facilitating the generation of better solutions. Furthermore, we theoretically prove that when the objective function is monotonic, DKESS can achieve the optimal approximation guarantee of 1 − e^−γ within polynomial time. Experimental results on two typical subset selection applications (i.e., sparse regression and unsupervised feature selection) demonstrate that the proposed DKESS outperforms state-of-the-art methods.

As nanometer-scale CMOS technology continues to advance, the susceptibility of memory cells to soft errors has significantly increased. Single-event multiple-node upsets (MNUs) have become a critical factor affecting integrated circuit reliability. This paper first proposes a self-recoverable double-node-upset (DNU) hardened latch (DSAR) based on the Soft-Error Aware Read-Decoupled (SAR) latch. Building upon this, a Quad-SAR latch capable of tolerating Quintuple-node upset (QtNU) is designed through dual DSAR redundancy and a two-stage Celement (CE) voting mechanism, achieving an optimized balance in area, power consumption, and delay. Furthermore, to address the high-impedance-state (HIS) sensitivity issue of C-elements, a Quint-SAR latch is achieved in this paper through the implementation of Quintuple-modular redundancy (QMR) combined with an Analog Voter (AV). This structure not only effectively tolerates QtNUs, but its incorporated Analog Voter features HIS insensitivity and offers significant low-overhead advantages compared to traditional voters. Extensive fault injection, PVT fluctuation, and negative bias temperature instability (NBTI) aging effect simulations verify the reliability of the proposed designs. HSPICE simulation results show that both the Quad-SAR and Quint-SAR latches possess QtNU tolerance capability and achieve a good balance in hardware overhead. Quad-SAR is suitable for low-power and low-delay requirements in non-HIS-sensitive scenarios, while Quint-SAR, with its HIS immunity, becomes the preferred solution for high-robustness scenarios. Compared to QNU-hardened and QtNU-hardened latches, their area-power-delay product (APDP) exhibits a decrease of 49.41% and 17.16%, respectively, and their setup time exhibits a decrease of 35.10% and 49.71%, respectively.

Endmember extraction (EE) plays an essential role in the unmixing of hyperspectral images, and many EE algorithms have been proposed. Among them, evolutionary algorithm (EA) based EE algorithms attract much attention due to the EA's powerful global search ability. However, hyperspectral EE problem itself is a constrained and sparse large-scale optimization problem, and it is difficult to search the optimal solutions efficiently. In this paper, to address this problem, we tackle this complex optimization problem from the view of evolutionary multi-tasking. Specifically, an evolutionary multi-tasking method for hyperspectral EE, termed EMT-HEE, is suggested, where two related tasks cooperate with each other to achieve endmembers with higher quality. In EMT-HEE, the original hyperspectral EE problem is regarded as the main task, whose aim is to obtain the final accurate endmembers. Meanwhile, an unconstrained task is constructed to assist the main task, and is used to explore the sparse large-scale search space thoroughly and avoid the local optima. To implement the evolutionary multi-tasking idea, two populations ($MP$ and $AP$) are evolved for the two tasks, respectively. During the evolving, a learning based solution generation strategy is suggested for the main task, which can produce high-quality solutions. Besides, a global search strategy with dynamic ranges is developed for the assisting task, which generates the solutions with more diversity. To make full use of the advantage of each task, a pair of knowledge transferring strategy (including the “assisting-to-main repairing strategy” and the “main-to-assisting enhancing strategy”) is proposed, which improves the performance of each task greatly. The competitiveness of EMT-HEE is validated on different hyperspectral datasets, and EMT-HEE can extract more accurate endmembers than the state-of-the-art EE algorithms.