Jinghui Zhong

Jinghui Zhong
South China University of Technology | SCUT · Department of Computer Science and Technology

PhD

About

100
Publications
11,570
Reads
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2,221
Citations
Citations since 2016
69 Research Items
1927 Citations
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
Additional affiliations
January 2013 - present
Nanyang Technological University
Position
  • Research Associate

Publications

Publications (100)
Article
For safety planning in crowd evacuation, it is important to predict the evacuation decisions made by different individuals and understand the reasons behind these decisions. To this end, this paper proposes an automated approach that can learn prioritized fuzzy decision rules from crowd data to predict and understand the evacuation decisions of a r...
Preprint
Automatic speaker verification (ASV) has been widely used in the real life for identity authentication. However, with the rapid development of speech conversion, speech synthesis algorithms and the improvement of the quality of recording devices, ASV systems are vulnerable for spoof attacks. In recent years, there have many works about synthetic an...
Article
Full-text available
The deployment of security guards in large public spaces is a promising research topic with a wide range of applications. Existing methods are mainly based on manual design approaches, which are neither effective nor flexible enough for large-scale scenarios. To address this issue, this paper proposes an evolutionary framework to automatically gene...
Article
Full-text available
Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that utilizes the program behavior to guide the search, called semantic GP (SGP), has achieved great success in solving SR prob...
Article
Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virt...
Article
Deploying guardrails near elevator entrances is an effective way to alleviate congestion and improve the flow rate in subway stations. How to properly design the guardrail layout is a complex black-box optimization problem. Existing methods are mainly based on manual design, which are highly dependent on the empirical experience of the designers an...
Article
Crowd navigation path planning is important in public scenes. Existing strategies are mainly based on manual design, which is not flexible or effective enough. This article proposes an evolutionary framework for automatic crowd navigation path planning in public scenes. The proposed framework contains a new fitness evaluation mechanism that can qua...
Article
Full-text available
Evolutionary algorithms possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extraction across distinct optimization task domains. Among t...
Chapter
Artificial neural network (ANN) is one of the most common methods for data regression. However, existing ANN based methods focus on fitting data with explicit relationships, where the output y can be explicitly expressed by the inputs x in the form of y=f(x). In contrast, implicit relationships (i.e., f(x,y)=0) are more expressive in that they can...
Article
Unmanned Aerial Vehicles (UAVs) have become powerful tools in modern military combat. How to properly allocate the tasks of heterogeneous UAVs in a combat is a fundamental and challenging problem. In this paper, we formulate the cooperative task allocation of heterogeneous UAVs as a constrained multi-objective optimization problem. To efficiently r...
Article
Evolutionary multitasking optimization (EMTO) is an emerging paradigm for solving several problems simultaneously. Due to the flexible framework, EMTO has been naturally applied to multi-objective optimization to exploit synergy among distinct multi-objective problem domains. However, most studies barely take into account the scenario where some pr...
Article
Automated scenario generation for virtual training has become an emerging research problem, as manual authoring is often time consuming and costly. In this paper, we present a mission-based scenario modeling and generation framework for virtual training. In particular, we consider the issue of how the timing of the events in a scenario can impact t...
Article
Symbolic regression is an active research topic that has various applications in data mining and knowledge discovery. Existing methods for symbolic regression mainly focus on mining explicit equations. In contrast, implicit equations are more flexible and powerful than explicit equations with regard to describing the relationships between variables...
Article
Full-text available
Genetic Programming (GP) is a popular and powerful evolutionary optimization algorithm that has a wide range of applications such as symbolic regression, classification and program synthesis. However, existing GPs often ignore the intrinsic structure of the ground truth equation of the symbolic regression problem. To improve the search efficacy of...
Article
Full-text available
Background Symbolic regression is one of the most common applications of genetic programming (GP), which is a popular evolutionary algorithm in automatic computer program generation. Despite existing success of GP on symbolic regression, the accuracy and efficiency of GP can still be improved especially on complicated symbolic regression problems,...
Article
Multiclass classification is one of the most fundamental tasks in data mining. However, traditional data mining methods rely on the model assumption, they generally can suffer from the overfitting problem on high dimension and low sample size (HDLSS) data. Trying to address multiclass classification problems on HDLSS data from another perspective,...
Article
One limitation of current data-driven automatic crowd modeling methods is that the models generated have low interpretability, which limits the practical applications of the models. In this article, we propose a new data-driven crowd modeling approach that can generate universal behavior rules with better interpretability. Higher interpretability h...
Article
At present, city logistics mostly adopts a two-echelon dispatching model which combines distribution centers located in suburbs and fixed satellites located in urban areas for distribution. However, both expensive rental fees and daily changes of customer demand in metropolitan areas make dispatching route generated by fixed satellites inefficient....
Article
Full-text available
Genetic programming (GP) is a popular and powerful optimization algorithm that has a wide range of applications, such as time series prediction, classification, data mining, and knowledge discovery. Despite the great success it enjoyed, selecting the proper primitives from high-dimension primitive set for GP to construct solutions is still a time-c...
Article
A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge transfer among different tasks, MFEA has demonstrated the capability to outperform its single-task counterpart in terms of both convergence speed and...
Article
Recently, evolutionary multitasking (EMT) has been proposed in the field of evolutionary computation as a new search paradigm, for solving multiple optimization tasks simultaneously. By sharing useful traits found along the evolutionary search process across different optimization tasks, the optimization performance on each task could be enhanced....
Article
Full-text available
Gene Expression Programming (GEP), a variant of Genetic Programming (GP), is a well established technique for automatic generation of computer programs. Due to the flexible representation, GEP has long been concerned as a classification algorithm for various applications. Whereas, GEP cannot be extended to multi-classification directly, and thus is...
Article
Maximizing the lifetime of wireless sensor networks (WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink sch...
Article
With the emergence of crowdshipping and sharing economy , vehicle routing problem with occasional drivers (VRPOD) has been recently proposed to involve occasional drivers with private vehicles for the delivery of goods. In this article, we present a generalized variant of VRPOD, namely, the vehicle routing problem with heterogeneous capacity, t...
Article
Economic dispatching of generating units in a power system can significantly reduce the energy cost of the system. However, the economic dispatch (ED) problem is highly constrained, and often has disconnected feasible regions because of various physical features. Enhancing population diversity is critical for the evolutionary approach to fully expl...
Article
Multi-task optimization is a hot research topic in the field of evolutionary computation. This paper proposes an efficient surrogate-assisted multi-task evolutionary framework (named SaEF-AKT) with adaptive knowledge transfer for multi-task optimization. In the proposed SaEF-AKT, several tasks which are computationally expensive are solved jointly...
Article
Target tracking is one of the most common applications in mobile sensor networks. However, since mobile sensors are often battery powered, determining how to schedule the movements of mobile sensors to reduce energy consumption remains an important and challenging task. In this paper, a partition-based target tracking framework with a modified cont...
Article
Test case prioritization (TCP) is a popular regression testing technique in software engineering field. The task of TCP is to schedule the execution order of test cases so that certain objective (e.g., code coverage) can be achieved quickly. In this article, we propose an efficient ant colony system framework for the TCP problem, with the aim of ma...
Article
Workflow scheduling problem (WSP) is a well-known combinatorial optimization problem, which is defined to assign a series of interconnected tasks to the available resources to meet user defined Quality of Service (QoS). Traditional guided random search methods and heuristic based methods are either require expensive computational cost or heavily re...
Article
Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task evolutionary algorithm (MaTEA), for many-task optimization. In the proposed MaTEA, an adaptive selection mechanism is proposed to select suitable “assisted” task for a given task by consi...
Article
Multi-modal optimization is an active research topic that has attracted increasing attention from evolutionary computation community. Particle swarm optimization (PSO) with niching technique is one of the most effective approaches for multi-modal optimization. However, in existing PSO with niching methods, the number of particles around different n...
Article
In this article, we propose a role-dependent (RD) data-driven modeling approach to simulate pedestrians’ motion in high-density scenes. It is commonly observed that pedestrians behave quite differently when walking in a dense crowd. Some people explore routes toward their destinations. Meanwhile, some people deliberately follow others, leading to l...
Article
Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multifactorial optimization (MFO) has...
Conference Paper
Test case prioritization which aims to improve the efficiency of regression testing by ordering test cases is an active research topic that has attracted increasing attention recently. This paper proposes an efficient Ant Colony System(ACS) framework to solve the coverage based test case prioritization problem. A tour based heuristic function and a...
Conference Paper
In contrast to the traditional single-task evolutionary algorithms, multi-factorial evolutionary algorithm (MFEA) has been proposed recently to conduct evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics of the tasks to be tackled by seamlessly transferring knowledge among them. Towards superior mult...
Conference Paper
Genetic programming(GP) is a powerful tool to solve Symbolic Regression that requires finding mathematic formula to fit the given observed data. However, existing GPs construct solutions based on building blocks (i.e., the terminal and function set) defined by users in an ad-hoc manner. The search efficacy of GP could be degraded significantly when...
Article
Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transfer...
Chapter
Implicit equation is loose in form, which makes it more powerful than explicit equation for data regression. The mainstream method for automatic implicit equation discovery is based on calculating derivatives. However, this derivative-based mechanism requires high time consumption and it is difficult to solve problems with sparse data. To solve the...
Chapter
This paper proposes a novel evolutionary regression framework with Gaussian process and adaptive segmentation strategy (named ES-GP) for regression problems. The proposed framework consists of two components, namely, the outer DE and the inner DE. The outer DE focuses on finding the best segmentation scheme, while the inner DE focuses on optimizing...
Article
We propose an automatic crowd control framework based on multi-objective optimisation of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for optimal overall cro...
Conference Paper
Maximizing the lifetime of Wireless Sensor Network (WSN) with a mobile sink is a challenging and important problem that has attracted increasing research attentions. In the literature, heuristic based approaches have been proposed to solve the problem, such as the Greedy Maximum Residual Energy (GMRE) based method. However, existing heuristic based...
Article
Abstract Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements and developments. A number of enhanced GEPs have been proposed to date and the real world applications that use them are also multiplying fast. In view...
Conference Paper
This paper proposes an Indicator-Based Multi-objective Gene Expression Programming (IBM-GEP) to solve Workflow Scheduling Problem (WSP). The key idea is to use Genetic Programming (GP) to learn heuristics to select resources for executing tasks. By using different problem instances for training, the IBM-GEP is capable of learning generic heuristics...
Conference Paper
Computationally expensive problem challenges the application of evolutionary algorithms (EAs) due to the long runtime. Distributed EAs on distributed resources for calculating the individual fitness value in paralllel is a promising method to reduce runtime. A crucial issue in distributed EAs is how to scheduling the individuals to the distributed...
Article
Designing suitable behavioral rules of agents so as to generate realistic behaviors is a fundamental and challenging task in many forms of computational modeling. This paper proposes a novel methodology to automatically generate a descriptive model, in the form of behavioral rules, from video data of human crowds. In the proposed methodology, the p...
Article
This paper proposes a novel sampling-based adaptive bounding evolutionary algorithm termed SABEA that is capable of dynamically updating the search space during the evolution process for continuous optimization problems. The proposed SABEA adopts two bounding strategies, namely fitness-based bounding and probabilistic sampling-based bounding, to se...
Conference Paper
Estimating the real positions of objects in images is a fundamental operation in many data-driven modeling approaches. However, due to the perspective principle, the positions extracted directly from images usually are not accurate enough. Traditional data-driven modeling approaches using these data may not be able to build satisfying models. To so...
Conference Paper
Multifactorial optimization (MFO) is a new paradigm proposed recently for evolutionary multi-tasking. In contrast to traditional evolutionary optimization approaches, which focus on solving only a single optimization problem at a time, MFO was proposed to solve multiple optimization problems simultaneously. It is contended that the concept of evolu...
Conference Paper
Recently, partitional clustering approaches based on Evolutionary Algorithms (EAs) have shown promising in solving the data clustering problems. However, with the nearest prototype (NP) rule as the method for decoding, most of them are only suitable for clustering datasets with convex (e.g. hyperspherical) clusters. In this paper, we propose an aut...
Article
Full-text available
This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architect...
Article
Software configuration, which aims to customize the software for different users (e.g., Linux kernel configuration), is an important and complicated task. In software product line engineering (SPLE), feature oriented domain analysis is adopted and feature model is used to guide the configuration of new product variants. In SPLE, product configurati...
Conference Paper
In this paper, we propose a role-dependent data-driven modeling approach to simulate pedestrians' motion in high density scenes. It is commonly observed that pedestrians behave quite differently when walking in dense crowd. Some people explore routes towards their destinations. Meanwhile, some people deliberately follow others, leading to lane form...
Article
Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a fitness evaluation methodology that integrates the p...
Article
This paper proposes a generic data-driven crowd modeling framework to generate crowd behaviors that can match the video data. The proposed framework uses a dual-layer mechanism to model the crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the g...
Conference Paper
Safety planning for crowd evacuation is an important and active research topic nowadays. One important issue is to devise the evacuation plans of individuals in emergency situations so as to reduce the total evacuation time. This paper proposes a novel evolutionary algorithm (EA)-based methodology, together with agent-based crowd simulation, to sol...
Article
Crowd modeling and simulation is an important and active research field, with a wide range of applications such as computer games, military training and evacuation modeling. One important issue in crowd modeling is model calibration through parameter tuning, so as to produce desired crowd behaviors. Common methods such as trial-and-error are time c...
Conference Paper
Agent-based modelling of human crowds has now become an important and active research field, with a wide range of applications such as military training, evacuation analysis and digital game. One of the significant and challenging tasks in agent-based crowd modelling is the design of decision rules for agents, so as to reproduce desired emergent ph...
Conference Paper
Full-text available
Automated scenario generation for virtual training has become an emerging research problem, as manual authoring is often time consuming and costly. In this paper, we present a mission-based scenario modeling and generation framework for virtual training. In particular, we consider the issue of how the timing of the events in a scenario can impact t...
Article
Full-text available
Railway timetable scheduling is a fundamental operational problem in the railway industry and has significant influence on the quality of service provided by the transport system. This paper explores the periodic railway timetable scheduling (PRTS) problem, with the objective to minimize the average waiting time of the transfer passengers. Unlike t...
Article
Differential Evolution is a new paradigm of evolutionary algorithm which has been widely used to solve nonlinear and complex problems. The performance of DE is mainly dependent on the parameter settings, which relate to not only characteristics of the specific problem but also the evolution state of the algorithm. Hence, determining the suitable pa...
Article
This paper addresses a complicated problem in project management termed the payment scheduling negotiation problem. The problem is a practical extension of the classical multi-mode resource constrained project scheduling problem and it considers the financial aspects of both the project client and contractor in a contracting project. The client and...
Article
In wireless sensor networks (WSNs), sensors near the sink can be burdened with a large amount of traffic, because they have to transmit data generated by themselves and those far away from the sink. Hence the sensors near the sink would deplete their energy much faster than the others, which results in a short network lifetime. Using mobile sink is...
Article
Full-text available
Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC al...
Conference Paper
Scheduling sensor activities is an effective way to prolong the lifetime of wireless sensor networks (WSNs). In this paper, we explore the problem of wake-up scheduling in WSNs where sensors have different lifetime. A novel local wake-up scheduling (LWS) strategy is proposed to prolong the network lifetime with full coverage constraint. In the LWS...
Conference Paper
Direct search (DS) and evolutionary algorithms (EAs) are two of the most representative branches of derivative-free optimization methods. However, traditional DS becomes deficient in multimodal problems, while EAs suffer from long computational time due to the blind search caused by randomness in evolutionary operators. This paper proposes a new de...
Conference Paper
Many real-world applications can be modeled as multi-objective optimization problems (MOPs). Applying differential evolution (DE) to MOPs is a promising research topic and has drawn a lot of attention in recent years. To search high-quality solutions for MOPs, this paper presents a robust adaptive DE (termed AS-MODE) with following two features. Fi...
Conference Paper
This paper proposes a multi-objective memetic algorithm, namely Mem-NSGA-II, to optimize the relay node placement problem. The network lifetime and the number of relay nodes are two objectives to be optimized. In Mem-NSGA-II, three new local search (LS) operations are designed and incorporated into the fast non-dominated genetic algorithm II (NSGA-...
Conference Paper
Extending Estimation of distribution algorithms (EDAs) to the continuous field is a promising and challenging task. With a single probabilistic model, most existing continuous EDAs usually suffer from the local stagnation or a low convergence speed. This paper presents an enhanced continuous EDA with multiple probabilistic models (MP-EDA). In the M...
Conference Paper
The automated synthesis and optimization of power electronic circuits (PECs) is a significant and challenging task in the field of power electronics. Traditional methods such as the gradient-based methods, the hill-climbing techniques and the genetic algorithms (GA), are either prone to local optima or not efficient enough to find highly accurate s...
Article
Applying Estimation of Distribution Algorithms (EDAs) to solve continuous problems is a significant and challenging task in the field of evolutionary computation. So far, various continuous EDAs have been developed based on different probability models. Initially, the EDAs based on a single Gaussian probability model are widely used but they have t...
Conference Paper
Full-text available
This paper proposes a novel orthogonal predictive local search (OPLS) to enhance the performance of the conventional genetic algorithms. OPLS operation predicts the most promising direction for the individuals to explore their neighborhood. It uses the orthogonal design method to sample orthogonal combinations to make the prediction. The resulting...
Conference Paper
Full-text available
A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is disp...