Feature selection has two major conflicting aims, i.e., to maximize the classification performance and to minimize the number of selected features to overcome the curse of dimensionality. To balance ...their trade-off, feature selection can be handled as a multi-objective problem. In this paper, a feature selection approach is proposed based on a new multi-objective artificial bee colony algorithm integrated with non-dominated sorting procedure and genetic operators. Two different implementations of the proposed approach are developed: ABC with binary representation and ABC with continuous representation. Their performance are examined on 12 benchmark datasets and the results are compared with those of linear forward selection, greedy stepwise backward selection, two single objective ABC algorithms and three well-known multi-objective evolutionary computation algorithms. The results show that the proposed approach with the binary representation outperformed the other methods in terms of both the dimensionality reduction and the classification accuracy.
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•The membrane was prepared by using a novel nature-inspired method.•The membrane possesses self-cleaning and antibacterial properties.•The membrane exhibits robust mechanical strength ...and stability.•The membrane features excellent biocompatible and UV-shielding.•Using this membrane can effectively separate various oil-water mixtures.
Fabrication of environmental-friendly, low-cost, and free-standing superhydrophobic nanofibrous membranes with additional functionalities such as self-cleaning and UV-shielding properties is highly demanded for oil-water separation. Herein, we describe the preparation of multifunctional superhydrophobic nanofibrous membrane by using a facile and novel nature-inspired method, i.e., plant polyphenol (tannic acid) metal complex is introduced to generate rough hierarchical structures on the surface of an electrospun polyimide (PI) nanofibrous membrane, followed by modification of poly (dimethylsiloxane) (PDMS). Taking an as-prepared tannic acid − Al3+-based superhydrophobic membrane as an example, it not only exhibits anti-impact, low-adhesive and self-cleaning functions, but also presents excellent performance in the separation of various oil-water mixtures. A high flux up to 6935 l m−2 h−1 with a separation efficiency of over 99% and the oil contents in water below 5 ppm is obtained even after repeating use for twenty separation cycles. Additionally, the membrane exhibits excellent UV-shielding property, attributing to the inherent UV-absorbing ability of tannic acid. Furthermore, the membrane also possesses additional properties including antibacterial activity, good biocompatibility, robust mechanical strength, and excellent resistance to various harsh conditions. These attractive properties of the as-prepared membrane make it a promising candidate for potential applications in industrial oil-contaminated water treatments and oil-water separation.
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Dynamic flexible job shop scheduling (JSS) is an important combinatorial optimization problem with complex routing and sequencing decisions under dynamic environments. Genetic programming (GP), as a ...hyperheuristic approach, has been successfully applied to evolve scheduling heuristics for JSS. However, its training process is time consuming, and it faces the retraining problem once the characteristics of job shop scenarios vary. It is known that multitask learning is a promising paradigm for solving multiple tasks simultaneously by sharing knowledge among the tasks. To improve the training efficiency and effectiveness, this article proposes a novel surrogate-assisted evolutionary multitask algorithm via GP to share useful knowledge between different scheduling tasks. Specifically, we employ the phenotypic characterization for measuring the behaviors of scheduling rules and building a surrogate for each task accordingly. The built surrogates are used not only to improve the efficiency of solving each single task but also for knowledge transfer in multitask learning with a large number of promising individuals. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all scenarios. In addition, the proposed algorithm manages to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.
Dynamic flexible job shop scheduling (JSS) is a challenging combinatorial optimization problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions ...need to be made simultaneously under the dynamic environments. Genetic programming (GP), as a hyperheuristic approach, has been successfully used to evolve scheduling heuristics for dynamic flexible JSS. However, in traditional GP, recombination between parents may disrupt the beneficial building blocks by choosing the crossover points randomly. This article proposes a recombinative mechanism to provide guidance for GP to realize effective and adaptive recombination for parents to produce offspring. Specifically, we define a novel measure for the importance of each subtree of an individual, and the importance information is utilized to decide the crossover points. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building blocks of one parent and incorporating good building blocks from the other. The proposed algorithm is examined on six scenarios with different configurations. The results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms on most tested scenarios, in terms of both final test performance and convergence speed. In addition, the rules obtained by the proposed algorithm have good interpretability.
In this paper, the author concerns two trace Trudinger-Moser inequalities and obtains the corresponding extremal functions on a compact Riemann surface (Σ,
g
) with smooth boundary
∂
Σ. Explicitly, ...let
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Math. Z.
,
250
, 2005, 363–386, Yang, Y., Moser-Trudinger trace inequalities on a compact Riemannian surface with boundary,
Pacific J. Math.
,
227
, 2006, 177–200 and Yang, Y., Extremal functions for Trudinger-Moser inequalities of Adimurthi-Druet type in dimension two,
J. Diff. Eq.
,
258
, 2015, 3161–3193.
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•The membrane can perform oily wastewater separation, stay stable under harsh environments and mechanical abrasion.•The modification layer was transparent and achieved UV-resistant ...property as well.•The preparation process was fast, facile and environmental-friendly.
As oily wastewater issues continue to grow, the treatment of oily wastewater has become urgent an emergency. However, present solutions are restricted by the limited performance of materials. An UV-resist and transparent coating consisting of PDMS and ZnO decorated on a highly stable and self-standing polyimide is reported in this work as a potential solution. The obtained fibrous membranes not only allow high-efficiency (higher than 99%) oily wastewater separation but also show superior UV-resistant activity. Moreover, the superoleophilicity and hydrophobicity of the composite membrane were confirmed to be stable under harsh conditions, and the transparent coating layer has the potential to be applied in other fields. The design of the UV-resistant, transparent and superoleophilic nanofibrous membrane is very practical, and will be quite promising for reducing oily environmental contaminations from waters.
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When learning from high-dimensional data for symbolic regression (SR), genetic programming (GP) typically could not generalize well. Feature selection, as a data preprocessing method, can potentially ...contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalization ability. However, in GP for high-dimensional SR, feature selection before learning is seldom considered. In this paper, we propose a new feature selection method based on permutation to select features for high-dimensional SR using GP. A set of experiments has been conducted to investigate the performance of the proposed method on the generalization of GP for high-dimensional SR. The regression results confirm the superior performance of the proposed method over the other examined feature selection methods. Further analysis indicates that the models evolved by the proposed method are more likely to contain only the truly relevant features and have better interpretability.
Convolutional neural networks hold state-of-the-art results for image classification, and many neural architecture search algorithms have been proposed to discover high performance convolutional ...neural networks. However, the use of neural architecture search for the discovery of skip-connection structures, an important element in modern convolutional neural networks, is limited within the literature. Furthermore, while many neural architecture search algorithms utilize performance estimation techniques to reduce computation time, empirical evaluations of these performance estimation techniques remain limited. This work focuses on utilizing evolutionary neural architecture search to examine the search space of networks, which follow a fundamental DenseNet structure, but have no fixed skip connections. In particular, a genetic algorithm is designed, which searches the space consisting of all networks between a standard feedforward network and the corresponding DenseNet. To design the algorithm, lower fidelity performance estimation of this class of networks is examined and presented. The final algorithm finds networks that are more accurate than DenseNets on CIFAR10 and CIFAR100, and have fewer trainable parameters. The structures found by the algorithm are examined to shed light on the importance of different types of skip-connection structures in convolutional neural networks, including the discovery of a simple skip-connection removal, which improves DenseNet performance on CIFAR10.
The problem of domain generalization is to take knowledge acquired from a number of related domains, where training data is available, and to then successfully apply it to previously unseen domains. ...We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition. The algorithm extends the standard denoising autoencoder framework by substituting artificially induced corruption with naturally occurring inter-domain variability in the appearance of objects. Instead of reconstructing images from noisy versions, MTAE learns to transform the original image into analogs in multiple related domains. It thereby learns features that are robust to variations across domains. The learnt features are then used as inputs to a classifier. We evaluated the performance of the algorithm on benchmark image recognition datasets, where the task is to learn features from multiple datasets and to then predict the image label from unseen datasets. We found that (denoising) MTAE outperforms alternative autoencoder-based models as well as the current state-of-the-art algorithms for domain generalization.
The massive growth of data in recent years has led challenges in data mining and machine learning tasks. One of the major challenges is the selection of relevant features from the original set of ...available features that maximally improves the learning performance over that of the original feature set. This issue attracts researchers’ attention resulting in a variety of successful feature selection approaches in the literature. Although there exist several surveys on unsupervised learning (e.g., clustering), lots of works concerning unsupervised feature selection are missing in these surveys (e.g., evolutionary computation based feature selection for clustering) for identifying the strengths and weakness of those approaches. In this paper, we introduce a comprehensive survey on feature selection approaches for clustering by reflecting the advantages/disadvantages of current approaches from different perspectives and identifying promising trends for future research.
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