Ke Li

University of Exeter

Author Statistics

Papers

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H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Sam Kwong

City University of Hong Kong

Xin Yao

University of Birmingham

Qingfu Zhang

City University of Hong Kong

Kalyanmoy Deb

Michigan State University

Geyong Min

University of Exeter

Miqing Li

University of Birmingham

Renzhi Chen

Academy of Military Medical Sciences

Ran Wang

Shenzhen University

Rongrong Ji

Xiamen University

Jinhua Zheng

Xiangtan University

Cooperative Institutions

Chinese Academy of Sciences

139

University of Chinese Academy of Sciences

Tsinghua University

Zhejiang University

University of Electronic Science and Technology of China

Shanghai Jiao Tong University

Huazhong University of Science and Technology

Peking University

Harbin Institute of Technology

Sun Yat-sen University

Author Statistics

Papers

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i-10 index

Research Field

Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization

IEEE Transactions on Evolutionary Computation (2018)

Ke Li Renzhi Chen Guangtao Fu Xin Yao

When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers.

Leverage (statistics)

Benchmark (surveying)

10.1109/tevc.2018.2855411

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Citations (472)

Evolutionary Art Attack For Black-Box Adversarial Example Generation

IEEE Transactions on Evolutionary Computation (2024)

Phoenix Williams Ke Li Geyong Min

Black box

10.1109/tevc.2024.3391063

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Citations (0)

Search-Based Software Engineering for Self-Adaptive Systems: Survey, Disappointments, Suggestions and Opportunities

arXiv (Cornell University) (2020)

Tao Chen Miqing Li Ke Li Kalyanmoy Deb

Search-Based Software Engineering (SBSE) is a promising paradigm that exploits the computational search to optimize different processes when engineering complex software systems. Self-adaptive system (SAS) is one category of such complex systems that permits to optimize different functional and non-functional objectives/criteria under changing environments (e.g., requirements and workload), which involves problems that are subject to search. In this regard, over years, there has been a considerable amount of work that investigates SBSE for SASs. In this paper, we provide the first systematic and comprehensive survey exclusively on SBSE for SASs, covering papers in 27 venues from 7 repositories, which eventually leads to several key statistics from the most notable 74 primary studies in this particular field of research. Our results, surprisingly, have revealed five disappointments that are of utmost importance and can result in serve consequences but have been overwhelmingly ignored in existing studies. We provide theoretical and/or experimental evidence to justify our arguments against the disappointments, present suggestions, and highlight the promising research opportunities towards their mitigation. We also elaborate on three other emergent, but currently under-explored opportunities for future work on SBSE for SASs. By mitigating the disappointments revealed in this work, together with the highlighted opportunities, we hope to be able to excite a much more significant growth in this particular research direction.

Sass

Search-based software engineering

10.48550/arxiv.2001.08236

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Citations (4)

Sparse Adversarial Attack via Bi-objective Optimization

Lecture notes in computer science (2023)

Phoenix Williams Ke Li Geyong Min

Minification

10.1007/978-3-031-27250-9_9

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Citations (1)

DaNuoYi: Evolutionary Multi-Task Injection Testing on Web Application Firewalls

IEEE Transactions on Software Engineering (2023)

Ke Li Heng Yang Willem Visser

Software testing

10.1109/tse.2023.3343716

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Citations (0)

Architecture Disentanglement for Deep Neural Networks

arXiv (Cornell University) (2020)

Jie Hu Liujuan Cao Qixiang Ye Tong Tong ShengChuan Zhang

Understanding the inner workings of deep neural networks (DNNs) is essential to provide trustworthy artificial intelligence techniques for practical applications. Existing studies typically involve linking semantic concepts to units or layers of DNNs, but fail to explain the inference process. In this paper, we introduce neural architecture disentanglement (NAD) to fill the gap. Specifically, NAD learns to disentangle a pre-trained DNN into sub-architectures according to independent tasks, forming information flows that describe the inference processes. We investigate whether, where, and how the disentanglement occurs through experiments conducted with handcrafted and automatically-searched network architectures, on both object-based and scene-based datasets. Based on the experimental results, we present three new findings that provide fresh insights into the inner logic of DNNs. First, DNNs can be divided into sub-architectures for independent tasks. Second, deeper layers do not always correspond to higher semantics. Third, the connection type in a DNN affects how the information flows across layers, leading to different disentanglement behaviors. With NAD, we further explain why DNNs sometimes give wrong predictions. Experimental results show that misclassified images have a high probability of being assigned to task sub-architectures similar to the correct ones. Code will be available at: https://github.com/hujiecpp/NAD.

Deep Neural Networks

Code (set theory)

Source

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Citations (0)

Deep Learning of Latent Edge Types from Relational Data

Kiarash Zahirnia Oliver Schulte Ke Li Ankita Sakhuja Parmis Naddaf

Many relational datasets, including relational databases, feature links of different types (e.g., actors act in movies, users rate movies), known as multi-relational, heterogeneous, or multi-layer graphs. Edge types/graph layers are often incompletely labeled. For example, IMDb lists Tom Cruise as a cast member of Mission Impossible, but not as its star. Inferring latent layers is useful for relational prediction tasks (e.g. predict Tom Cruiseâs salary or his presence in other movies). This paper describes a Latent Layer Generative Framework - LLGF that extends graph encoder-decoder architectures to include latent layers. The decoder treats the observed edge type signalas a linear combination of latent layers. The encoder infers parallel node representations, one for each latent layer. We evaluate our proposed framework on six benchmark graph learning datasets. Qualitative evidence indicates that LLGF recovers ground truth layers well. For link prediction as a downstream task, we find that extending Variational Graph Auto-Encoders with LLGF increases link prediction accuracy compared to state-of-theart graph Variational Auto-Encoders (up to 6% AUC depending on the dataset).

Benchmark (surveying)

Feature (linguistics)

10.21428/594757db.06b4cfb6

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Citations (0)

Non-Adversarial Image Synthesis With Generative Latent Nearest Neighbors

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

Yedid Hoshen Ke Li Jitendra Malik

Unconditional image generation has recently been dominated by generative adversarial networks (GANs). GAN methods train a generator which regresses images from random noise vectors, as well as a discriminator that attempts to differentiate between the generated images and a training set of real images. GANs have shown amazing results at generating realistic looking images. Despite their success, GANs suffer from critical drawbacks including: unstable training and mode-dropping. The weaknesses in GANs have motivated research into alternatives including: variational auto-encoders (VAEs), latent embedding learning methods (e.g. GLO) and nearest-neighbor based implicit maximum likelihood estimation (IMLE). Unfortunately at the moment, GANs still significantly outperform the alternative methods for image generation. In this work, we present a novel method - Generative Latent Nearest Neighbors (GLANN) - for training generative models without adversarial training. GLANN combines the strengths of IMLE and GLO in a way that overcomes the main drawbacks of each method. Consequently, GLANN generates images that are far better than GLO and IMLE. Our method does not suffer from mode collapse which plagues GAN training and is much more stable. Qualitative results show that GLANN outperforms a baseline consisting of 800 GANs and VAEs on commonly used datasets. Our models are also shown to be effective for training truly non-adversarial unsupervised image translation.

Discriminator

Mode (computer interface)

10.1109/cvpr.2019.00596

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Citations (49)

DeepSQLi: Deep Semantic Learning for Testing SQL Injection

arXiv (Cornell University) (2020)

Muyang Liu Ke Li Tao Chen

Security is unarguably the most serious concern for Web applications, to which SQL injection (SQLi) attack is one of the most devastating attacks. Automatically testing SQLi vulnerabilities is of ultimate importance, yet is unfortunately far from trivial to implement. This is because the existence of a huge, or potentially infinite, number of variants and semantic possibilities of SQL leading to SQLi attacks on various Web applications. In this paper, we propose a deep natural language processing based tool, dubbed DeepSQLi, to generate test cases for detecting SQLi vulnerabilities. Through adopting deep learning based neural language model and sequence of words prediction, DeepSQLi is equipped with the ability to learn the semantic knowledge embedded in SQLi attacks, allowing it to translate user inputs (or a test case) into a new test case, which is semantically related and potentially more sophisticated. Experiments are conducted to compare DeepSQLi with SQLmap, a state-of-the-art SQLi testing automation tool, on six real-world Web applications that are of different scales, characteristics and domains. Empirical results demonstrate the effectiveness and the remarkable superiority of DeepSQLi over SQLmap, such that more SQLi vulnerabilities can be identified by using a less number of test cases, whilst running much faster.

SQL Injection

10.48550/arxiv.2005.11728

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Citations (5)

Evolutionary Multiobjective Optimization-Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection

IEEE Transactions on Evolutionary Computation (2017)

Ran Cheng Miqing Li Ke Li Xin Yao

Recently, by taking advantage of evolutionary multiobjective optimization techniques in diversity preservation, the means of multiobjectivization has attracted increasing interest in the studies of multimodal optimization (MMO). While most existing work of multiobjectivization aims to find all optimal solutions simultaneously, in this paper, we propose to approximate multimodal fitness landscapes via multiobjectivization, thus providing an estimation of potential optimal areas. To begin with, an MMO problem is transformed into a multiobjective optimization problem (MOP) by adding an adaptive diversity indicator as the second optimization objective, and an approximate fitness landscape is obtained via optimization of the transformed MOP using a multiobjective evolutionary algorithm. Then, on the basis of the approximate fitness landscape, an adaptive peak detection method is proposed to find peaks where optimal solutions may exist. Finally, local search is performed inside the detected peaks on the approximate fitness landscape. To assess the performance of the proposed algorithm, extensive experiments are conducted on 20 multimodal test functions, in comparison with three state-of-the-art algorithms for MMO. Experimental results demonstrate that the proposed algorithm not only shows promising performance in benchmark comparisons, but also has good potential in assisting preference-based decision-making in MMO.

Fitness landscape

Benchmark (surveying)

Fitness approximation

10.1109/tevc.2017.2744328

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Citations (109)