Yuan Yuan

Yuan Yuan
Beihang University (BUAA) | BUAA · Department of Computer Science and Technology

PhD

About

48
Publications
16,629
Reads
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2,355
Citations
Additional affiliations
December 2016 - present
Michigan State University
Position
  • PostDoc Position
October 2015 - December 2016
Nanyang Technological University
Position
  • Research Associate
October 2015 - present
Tsinghua University
Position
  • PhD Student

Publications

Publications (48)
Article
Full-text available
In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chrom...
Article
Full-text available
Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently su...
Article
Full-text available
The decomposition-based multiobjective evolutionary algorithms generally make use of aggregation functions to decompose a multiobjective optimization problem into multiple single-objective optimization problems. However, due to the nature of contour lines for the adopted aggregation functions, they usually fail in preserving the diversity in high-d...
Article
Full-text available
Recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locatio...
Article
Full-text available
Many-objective optimization problems bring great difficulties to the existing multiobjective evolutionary algorithms, in terms of selection operators, computational cost, visualization of the high-dimensional trade-off front, and so on. Objective reduction can alleviate such difficulties by removing the redundant objectives in the original objectiv...
Preprint
Program synthesis aims to {\it automatically} find programs from an underlying programming language that satisfy a given specification. While this has the potential to revolutionize computing, how to search over the vast space of programs efficiently is an unsolved challenge in program synthesis. In cases where large programs are required for a sol...
Preprint
Full-text available
In this paper we introduce Shackleton as a generalized framework enabling the application of linear genetic programming -- a technique under the umbrella of evolutionary algorithms -- to a variety of use cases. We also explore here a novel application for this class of methods: optimizing sequences of LLVM optimization passes. The algorithm underpi...
Poster
Full-text available
LLVM IR (low-level virtual machine intermediate representation) is an intermediate step in the compilation of computer code. LLVM compilers allow optimization by using a sequence of steps (passes) to improve run-time or other criteria. Genetic Programming (GP) is an algorithm that is inspired by the natural selection process and can automatically g...
Article
The papers in this special issue on privacy and security in computational intelligence. The advanced in the state-of-the-art computing paradigms and infrastructure such as cloud computing, Internet of Things (IoT) and their fusion fog computing, has enabled a variety of large-scale applications where big data are collected, transmitted, stored, pro...
Chapter
The automation of program repair can be coached in terms of search algorithms. Repair templates derived from common bug-fix patterns can be used to determine a promising search space with potentially many correct patches, a space that can be effectively explored by GP methods. Here we propose a new repair system, ARJA-p, extended from our earlier A...
Chapter
When a test suite is considered as the specification, the paradigm is called test-suite based repair Monperrus (ACM Comput Surv 51(1):17, 2018). The test suite should contain at least one negative (i.e., initially failing) test that triggers the bug to be fixed and a number of positive (i.e., initially passing) tests that define the expected progra...
Article
Full-text available
For the issue of users’ sensibility to the QoS (Quality of Service) of multimedia applications, cloudlet has emerged as a novel paradigm which provides closer computing resources to users to improve the performance of multimedia applications and meet the QoS demands of users. However, the increasing users’ requirements of migrating tasks pose a cha...
Article
Full-text available
Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due t...
Article
Bug repair is a major component of software maintenance, which requires a huge amount of manpower. Evolutionary computation, particularly genetic programming (GP), is a class of promising techniques for automating this time-consuming and expensive process. Although recent research in evolutionary program repair has made significant progress, major...
Article
Full-text available
By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and ed...
Chapter
Nowadays, with the advances in wireless communication, the mobile devices are becoming important due to various applications which provide mobile users with plentiful services in the devices. The mobile devices can hardly complete all the computing tasks as they have limitations on the battery capacity, physical size, etc. In order to release these...
Article
The evolutionary algorithms have been used to improve the energy consumption of embedded software by searching the optimal compilation options of GCC compiler. However, these algorithms do not consider the complex multivariate interactions between compilation options, which has negative effect on solution quality and convergence rate. Furthermore,...
Conference Paper
This paper presents an automatic software repair system that combines the characteristic components of several typical evolutionary computation based repair approaches into a unified repair framework so as to take advantage of their respective component strengths. We exploit both the redundancy assumption and repair templates to create a search spa...
Conference Paper
Full-text available
Many applications such as hyper-parameter tunning in Machine Learning can be casted to multiobjective black-box problems and it is challenging to optimize them. Bayesian Optimization (BO) is an effective method to deal with black-box functions. This paper mainly focuses on balancing exploration and exploitation in multi-objective black-box optimiza...
Conference Paper
Full-text available
Expensive black-box problems are usually optimized by Bayesian Optimization (BO) since it can reduce evaluation costs via cheaper surrogates. The most popular model used in Bayesian Optimization is the Gaussian process (GP) whose posterior is based on a joint GP prior built by initial observations, so the posterior is also a Gaussian process. Obser...
Article
The Internet of connected vehicles (IoV) is employed to collect real-time traffic conditions for transportation control systems, and the computing tasks are available to be offloaded from the vehicles to the edge computing devices (ECDs) for implementation. Despite numerous benefits of IoV and ECDs, the wireless communication for computation offloa...
Article
Cloudlet is a novel computing paradigm, introduced to the mobile cloud service framework, which moves the computing resources closer to the mobile users, aiming to alleviate the communication delay between the mobile devices and the cloud platform and optimize the energy consumption for mobile devices. Currently, the mobile applications, modeled by...
Article
Full-text available
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algor...
Chapter
Full-text available
Over the past decades, Evolutionary Computation (EC) has surfaced as a popular paradigm in the domain of computational intelligence for global optimization of complex multimodal functions. The distinctive feature of an Evolutionary Algorithm (EA) is the emergence of powerful implicit parallelism as an offshoot of the simple rules of population-base...
Conference Paper
Full-text available
Evolutionary computation (EC) has gained increasing popularity in dealing with permutation-based combinatorial optimization problems (PCOPs). Traditionally, EC focuses on solving a single optimization task at a time. However, in complex multi-echelon supply chain networks (SCNs), there usually exist various kinds of PCOPs at the same time, e.g., tr...
Conference Paper
Full-text available
Reference-point based multi-objective evolutionary algorithms (MOEAs) have shown promising performance in many-objective optimization. However, most of existing research within this area focused on improving the environmental selection procedure, and little work has been done on the effect of variation operators. In this paper, we conduct an experi...
Article
In evolutionary multi-objective optimization, balancing convergence and diversity remains a challenge and especially for many-objective (three or more objectives) optimization problems (MaOPs). To improve convergence and diversity for MaOPs, we propose a new approach: clustering-ranking evolutionary algorithm (crEA), where the two procedures (clust...
Article
Full-text available
Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the selection pressure based on Pareto-dominance degrades severely with the number of objectives increasing. Ver...
Article
Full-text available
In this paper, a new framework, called ensemble fitness ranking (EFR), is proposed for evolutionary many-objective optimization that allows to work with different types of fitness functions and ensemble ranking schemes. The framework aims to rank the solutions in the population more appropriately by combing the ranking results from many simple indi...
Article
Full-text available
Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. Beta distribution is the conjugate priori for binomial distribution. Besides, we introduced a lea...
Conference Paper
Full-text available
The quantum-inspired evolutionary algorithm (QEA) uses several quantum computing principles to optimize problems on a classical computer. QEA possesses a number of quantum individuals, which are all probability vectors. They work well for linear problems but fail on problems with strong interactions among variables. Moreover, many optimization prob...
Conference Paper
Full-text available
In this paper, a new memetic algorithm (MA) is proposed for the muti-objective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload and critical workload. By using well-designed chromosome encoding/decoding scheme and genetic operators, the non-dominated sorting genetic algorithm II (NSGA-II) i...
Article
Full-text available
In this paper, a novel hybrid harmony search (HHS) algorithm based on the integrated approach, is proposed for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize makespan. First of all, to make the harmony search (HS) algorithm adaptive to the FJSP, the converting techniques are developed to convert the continuou...
Conference Paper
The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem (JSP), where each operation is allowed to be processed by any machine from a given set, rather than one specified machine. In this paper, two algorithm modules, namely, hybrid harmony search (HHS) and large neighborhood search (LNS) are...

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