Search Results - "KAY CHEN TAN"

A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization by GOH, Chi-Keong, KAY CHEN TAN

“…In addition to the need for satisfying several competing objectives, many real-world applications are also dynamic and require the optimization algorithm to…”
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A survey on evolutionary computation for complex continuous optimization by Zhan, Zhi-Hui, Shi, Lin, Tan, Kay Chen, Zhang, Jun

“…Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet…”
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A Multi-Facet Survey on Memetic Computation by Chen, Xianshun, Ong, Yew-Soon, Lim, Meng-Hiot, Tan, Kay Chen

“…Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of…”
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Evolutionary Multitasking via Explicit Autoencoding by Feng, Liang, Zhou, Lei, Zhong, Jinghui, Gupta, Abhishek, Ong, Yew-Soon, Tan, Kay-Chen, Qin, A. K.

“…Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary…”
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Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking by Feng, Liang, Zhou, Lei, Gupta, Abhishek, Zhong, Jinghui, Zhu, Zexuan, Tan, Kay-Chen, Qin, Kai

“…With the emergence of crowdshipping and sharing economy , vehicle routing problem with occasional drivers (VRPOD) has been recently proposed to involve…”
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A Knee Point Driven Evolutionary Algorithm for Multiobjective Bilevel Optimization by Chen, Jiaxin, Ding, Jinliang, Li, Ke, Chen Tan, Kay, Chai, Tianyou

“…Bilevel optimization is a special type of optimization in which one problem is embedded within another. The bilevel optimization problem (BLOP) of which both…”
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Hybrid evolutionary computation methods for quay crane scheduling problems by Nguyen, Su, Zhang, Mengjie, Johnston, Mark, Chen Tan, Kay

“…Quay crane scheduling is one of the most important operations in seaport terminals. The effectiveness of this operation can directly influence the overall…”
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A Survey on Evolutionary Neural Architecture Search by Liu, Yuqiao, Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G., Tan, Kay Chen

“…Deep neural networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is…”
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A Cost-Sensitive Deep Belief Network for Imbalanced Classification by Zhang, Chong, Tan, Kay Chen, Li, Haizhou, Hong, Geok Soon

“…Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is…”
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Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction by Muruganantham, Arrchana, Kay Chen Tan, Vadakkepat, Prahlad

“…Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art…”
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Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization by Tian, Ye, Zhang, Yajie, Su, Yansen, Zhang, Xingyi, Tan, Kay Chen, Jin, Yaochu

“…Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary…”
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Solving Large-Scale Multiobjective Optimization Problems With Sparse Optimal Solutions via Unsupervised Neural Networks by Tian, Ye, Lu, Chang, Zhang, Xingyi, Tan, Kay Chen, Jin, Yaochu

“…Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary algorithms to approximate the optimal solutions of large-scale…”
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A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks by Wu, Jibin, Chua, Yansong, Zhang, Malu, Li, Guoqi, Li, Haizhou, Tan, Kay Chen

“…Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due…”
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A Generic Deep-Learning-Based Approach for Automated Surface Inspection by Ren, Ruoxu, Hung, Terence, Tan, Kay Chen

“…Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly…”
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Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment by Lu, Zhichao, Cheng, Ran, Jin, Yaochu, Tan, Kay Chen, Deb, Kalyanmoy

“…The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks…”
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A Survey on Evolutionary Construction of Deep Neural Networks by Zhou, Xun, Qin, A. K., Gong, Maoguo, Tan, Kay Chen

“…Automated construction of deep neural networks (DNNs) has become a research hot spot nowadays because DNN's performance is heavily influenced by its…”
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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks by Yang, Shangshang, Tian, Ye, He, Cheng, Zhang, Xingyi, Tan, Kay Chen, Jin, Yaochu

“…It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based…”
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Progressive Tandem Learning for Pattern Recognition With Deep Spiking Neural Networks by Wu, Jibin, Xu, Chenglin, Han, Xiao, Zhou, Daquan, Zhang, Malu, Li, Haizhou, Tan, Kay Chen

“…Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational…”
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Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics by Chong Zhang, Pin Lim, Qin, A. K., Kay Chen Tan

“…In numerous industrial applications where safety, efficiency, and reliability are among primary concerns, condition-based maintenance (CBM) is often the most…”
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Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs) by He, Cheng, Huang, Shihua, Cheng, Ran, Tan, Kay Chen, Jin, Yaochu

“…Recently, increasing works have been proposed to drive evolutionary algorithms using machine-learning models. Usually, the performance of such model-based…”
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