The iterative search process of evolutionary algorithms (EAs) encapsulates optimization knowledge within historical populations and fitness evaluations. Effective utilization of this knowledge is crucial for facilitating knowledge transfer and online adaptation. However, current research typically addresses these goals in isolation and faces distinct limitations: evolutionary sequential transfer optimization often suffers from incomplete utilization of prior knowledge, while adaptive strategies, utilizing real-time knowledge, are limited to tailoring specific evolutionary operators. To simultaneously achieve these two capabilities, we introduce the Optimization Knowledge Adaptation Evolutionary Model (OKAEM), a unified learnable evolutionary framework capable of adaptively updating parameters based on available optimization knowledge. By parameterizing evolutionary operators via attention mechanisms, OKAEM enables learnable update rules that facilitate the utilization of optimization knowledge via two phases: pre-training to integrate extensive prior knowledge for efficient transfer, and adaptive optimization to dynamically update parameters based on real-time knowledge. Experimental results confirm that OKAEM significantly outperforms state-of-the-art sequential transfer methods across 12 transfer scenarios via pre-training, and surpasses advanced learnable EAs solely through its self-tuning mechanism in prior-free settings. Beyond demonstrating practical utility in prompt tuning for vision-language models, ablation studies validate the necessity of the learnable components, while visualization analyses reveal the model's capacity to autonomously discover interpretable evolutionary principles.

Due to the domain shift, it is challenging to achieve ideal experimental results for cross-domain few-shot learning (FSL) in hyperspectral image (HSI) classification. Most existing FSL algorithms are impacted by the limited samples, and they do not effectively leverage the representations from different layers of the network. Therefore, this article proposes an FSL based on embedded self-distillation and adaptive Wasserstein (ESAW-FSL) distance for HSI classification. First, the embedding self-distillation network is proposed in the feature extraction process of the source domain (SD) and the target domain (TD). The embedding self-distillation network utilizes self-distillation from different perspectives to get discriminative features. In the SD, the mask evaluation of embedded features is employed to guarantee the learning of guiding features. Second, a domain adaptation based on adaptive Wasserstein distance is designed to alleviate the domain shift problem between the domains. A lightweight feature correlation network learns the comprehensive cost matrix in the Wasserstein distance adaptively, and the obtained cost matrix helps achieve domain adaptation by an iterative algorithm. Finally, a focal loss based on double softening is adopted in the process of FSL. The probability is double softened to improve the ratio of correctly classifying hard samples. Experiments are conducted on three widely used hyperspectral datasets and compared with six state-of-the-art algorithms. The overall accuracy (OA) and average accuracy (AA) are achieved in multiple experiments, demonstrating the effectiveness of ESAW-FSL.

Markov Random Fields (MRF) effectively capture local contextual information by modeling the spatial dependencies between pixels, which helps highlight details and enhances segmentation smoothness. To fully exploit MRF for synthetic aperture radar (SAR) image segmentation, we propose a novel edge-enhanced cascaded MRF (ECMRF) approach. Specifically, we introduce multiple edge-constrained filters to emphasize SAR image boundaries and provide relatively clean features. Building on this, we present a cascaded MRF framework that sequentially integrates region-level and pixel-level segmentation with feature perturbation and fusion to generate the final segmentation output. The framework comprises four key components: (1) a region-level MRF, regulated by edge features, to achieve precise region segmentation; (2) a pixel-level MRF with selective label smoothing to refine edges and reduce noise clusters; (3) equal-channel feature perturbation to increase feature diversity; and (4) a random probability-based feature fusion scheme to merge the input features. Experimental results demonstrate that our ECMRF outperforms six state-of-the-art comparable methods, underscoring its competitive performance.

Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities. However, manually determining the suitable Transformer architecture for a specific graph dataset or task requires extensive expert knowledge and laborious trials. This article proposes an evolutionary graph Transformer architecture search (EGTAS) framework to automate the construction of strong graph Transformers. We build a comprehensive graph Transformer search space with the micro-level and macro-level designs. EGTAS evolves graph Transformer topologies at the macro level and graph-aware strategies at the micro level. Furthermore, a surrogate model based on generic architectural coding is proposed to directly predict the performance of graph Transformers, substantially reducing the evaluation cost of evolutionary search. We demonstrate the efficacy of EGTAS across a range of graph-level and node-level tasks, encompassing both small-scale and large-scale graph datasets. Experimental results and ablation studies show that EGTAS can construct high-performance architectures that rival state-of-the-art manual and automated baselines.

Solving the complex challenges of sophisticated terrain and multi-scale targets in remote sensing (RS) images requires a synergistic combination of Transformer and convolutional neural network (CNN). However, crafting effective CNN architectures remains a major challenge. To address these difficulties, this study introduces the knowledge guided evolutionary Transformer for RS scene classification (Evo RSFormer). It amalgamates adaptive evolutionary CNN (Evo CNN) with Transformers in a hybrid strategy synergistically, which combines fine-grained local feature extraction of CNNs with long-range contextual dependency modeling of Transformers. Furthermore, for the development of Evo CNN blocks, this paper presents a knowledge-guided adaptive efficient multi-objective evolutionary neural architecture search (MOE2-NAS) strategy. This approach markedly diminishes the labor-intensive characteristics associated with traditional CNN design, striking a balance for both accuracy and compactness. Additionally, by leveraging domain knowledge from natural scene analysis into the RS field, MOE2-NAS facilitates the efficiency of classical NAS. It utilizes a priori knowledge to generate promising initial solutions and constructs a surrogate model for efficient search. The effectiveness of the proposed Evo RSFormer has been rigorously tested on various benchmark RS datasets, including UC Merced, NWPU45, and AID. Empirical results strongly support the superiority of Evo RSFormer over existing methods. Furthermore, experiments on MOE2-NAS have been studied to confirm the important role of knowledge guidance in improving the efficiency of NAS.

The construction of machine learning models involves many bi-level multiobjective optimization problems (BL-MOPs), where upper-level (UL) candidate solutions must be evaluated via training weights of a model in the lower level (LL). Due to the Pareto optimality of subproblems and the complex dependency across UL solutions and LL weights, a UL solution is feasible if and only if the LL weight is Pareto optimal. It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution. This article proposes a bi-level multiobjective learning framework (BLMOL), coupling the above decision-making process with the optimization process of the upper-level MOP (UL-MOP) by introducing LL preference$\boldsymbol {r}$. Specifically, the UL variable and$\boldsymbol {r}$are simultaneously searched to minimize multiple UL objectives by evolutionary multiobjective algorithms. The LL weight with respect to$\boldsymbol {r}$is trained to minimize multiple LL objectives via gradient-based preference multiobjective algorithms. In addition, the preference surrogate model is constructed to replace the expensive evaluation process of the UL-MOP. We consider a novel case study on multitask graph neural topology search. It aims to find a set of Pareto topologies and their Pareto weights, representing different tradeoffs across tasks at UL and LL, respectively. The found graph neural network is employed to solve multiple tasks simultaneously, including graph classification, node classification, and link prediction. Experimental results demonstrate that BLMOL can outperform some state-of-the-art algorithms and generate well-representative UL solutions and LL weights.

Superpixel segmentation algorithms are predominently based on simple linear iterative clustering (SLIC), and treat homogeneous and complex regions equally. This can lead to suboptimal segmentation results, especially in complex images with multiple objects. We address this problem by proposing an SAR image segmentation algorithm based on complicated region-sensitive adaptive superpixel generation and hybrid edge correction (RSASGEC). First, a dynamic initialization algorithm for superpixel seeds based on region complexity is designed. Specifically, a new superpixel representation structure for superpixel seeds is constructed by combining superpixel complexity and the number of contained pixels. The algorithm gives priority to regions with high complexity, dynamically selecting the region with the highest complexity for further partitioning. This results in a dense distribution of superpixel seeds in complex regions, and sparse distributions in homogeneous regions with low complexity. Second, an iterative superpixel segmentation process based on an adaptive energy function is proposed. The Lagrange multiplier mathematical strategy is employed to optimize the adaptive energy function within an adjustable search window, resulting in more compact superpixel segmentation. Finally, a label correction method, based on edge mixture model constraints, is proposed for postprocessing. By integrating edge information from the Gaussian edge detector and the Canny algorithm as constraints, this method leverages majority voting and region growth methods to mitigate edge noise and outliers, refining the superpixel labels. The RSASGEC algorithm is verified in experiments, using one simulated image and six real SAR images. The results indicate that RSASGEC outperforms six representative algorithms, achieving more satisfactory segmentation performance.

Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut-off value to distinguish whether the connections exist or not. Besides, their performances on large-scale networks are far from satisfactory. Considering the reconstruction error and sparsity as two objectives, this article proposes a subspace learning-based evolutionary multiobjective network reconstruction algorithm, called SLEMO-NR, to solve the aforementioned problems. In the evolutionary process, we assume that binary-coded individuals obey the Bernoulli distribution and can use the probability and natural parameter as alternative representations. Moreover, our approach utilizes the logistic principal component analysis (LPCA) to learn a subspace containing the features of the network structure. The offspring solutions are generated in the learned subspace and then can be mapped back to the original space via LPCA. Benefitting from the alternative representations, a preference-based local search operator (PLSO) is proposed to concentrate on finding solutions approximate to the true sparsity. The experimental results on synthetic networks and six real-world networks demonstrate that, due to the well-learned network structure subspace and the preference-based strategy, our approach is effective in reconstructing large-scale networks compared to six existing methods.

Deep representation learning has improved automatic remote change detection (RSCD) in recent years. Existing methods emphasize primarily convolutional neural networks (CNNs) or Transformer-based networks. However, most of them neither effectively combine CNNs and Transformer nor use prior geometric information to refine regions. In this paper, a novel geometric representation Transformer (GeoFormer) is proposed for high-resolution RSCD. GeoFormer utilizes convolutional information to guide the Transformer by employing geometric prior knowledge. Specifically, the proposed GeoFormer consists of three carefully designed components: the geometric-based Swin Transformer (Geo-Swin Transformer) encoder, the Laplace attention fusion (LAFusion) module, and the UNet++CD decoder. Firstly, Geo-Swin Transformer is a novel designed non-local Siamese encoder that combines geometric convolution with Transformer to provide local geometric representation information for remote contextual features. Then, a LAFusion module is proposed to achieve robust bi-temporal feature fusion, which is founded on attention mechanism and edge information. Finally, UNet++CD decodes fine-grained information from the fused features by dense multiscale upsampling process. Experimental results demonstrate that the proposed GeoFormer performs better than benchmark methods on four change detection datasets (LEVIR-CD, WHU-CD, DSIFN-CD, and CDD) and is able to detect the edges of change regions more precisely. Our code is available at https://github.com/Jiaxzhao/GeoFormer.

Due to the multilayer nature of real-world systems, the problem of inferring multilayer network structures from nonlinear and complex dynamical systems is prominent in many fields, including engineering, biological, physical, and computer sciences. Many network reconstruction methods have been proposed to address this problem, but none of them consider the similarities among network reconstruction tasks at different component layers, which are inspired by topology correlations and dynamic couplings among different component layers. This article develops an evolutionary multitasking multilayer network reconstruction framework to make use of the correlations among different component layers to improve the reconstruction performance; we refer to this framework as EM2MNR. In EM2MNR, the multilayer network reconstruction problem is first established as a multitasking multilayer network reconstruction problem, where the goal of each task is to reconstruct the network structure of a component layer. In addition, multitasking multilayer network reconstruction problems are high dimensional, but existing evolutionary multitasking algorithms may have poor performance when dealing with optimization problems with a high-dimensional search space. Inspired by the sparsity of multilayer networks, EM2MNR employs the restricted Boltzmann machine to extract low effective features from the original decision space and then decides whether to conduct knowledge transfer on these features. To verify the performance of EM2MNR, this article also designs a test suite for multilayer network reconstruction problems. The experimental results demonstrate the significant improvement obtained by the proposed EM2MNR framework on 96 multilayer network reconstruction problems.

Evolutionary multitask optimization (EMTO) has recently attracted widespread attention in the evolutionary computation community, which solves two or more tasks simultaneously to improve the convergence characteristics of tasks when individually optimized. Effective knowledge between tasks is transferred by taking advantage of the parallelism of population-based search. Without any prior knowledge about tasks, it is a challenging problem of how to adaptively transfer effective knowledge between tasks and reduce the impact of negative transfer in EMTO. However, these two issues are rarely studied simultaneously in the existing literature. Besides, in complex many-task environments, the potential relationships among individuals from highly diverse populations associated with tasks directly determine the effectiveness of cross-task knowledge transfer. Keeping those in mind, we propose a multitask evolutionary algorithm based on anomaly detection (MTEA-AD). Specifically, each task is assigned a population and an anomaly detection model. Each anomaly detection model is used to learn the relationship among individuals between the current task and the other tasks online. Individuals that may carry negative knowledge are identified as outliers, and candidate transferred individuals identified by the anomaly detection model are selected to assist the current task, which may carry common knowledge across the current task and other tasks. Furthermore, to realize the adaptive control of the degree of knowledge transfer, the successfully transferred individuals that survive to the next generation through the elitism are used to update the anomaly detection parameter. The fair competition between offspring and candidate transferred individuals can effectively reduce the risk of negative transfer. Finally, the empirical studies on a series of synthetic benchmarks and a practical study are conducted to verify the effectiveness of MTEA-AD. The experimental results demonstrate that our proposal can adaptively adjust the degree of knowledge transfer through the anomaly detection model to achieve highly competitive performance compared to several state-of-the-art EMTO methods.