Search Results for author: Xin Yao

Found 72 papers, 19 papers with code

The Vision of Self-Evolving Computing Systems

no code implementations14 Apr 2022 Danny Weyns, Thomas Baeck, Rene Vidal, Xin Yao, Ahmed Nabil Belbachir

We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions.

Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems

1 code implementation8 Apr 2022 Daniel Herring, Michael Kirley, Xin Yao

Our framework is based on an extension of PlatEMO, allowing for the reproduction of results and performance measurements across a range of dynamic settings and problems.

Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank

no code implementations6 Apr 2022 Ke Li, Guiyu Lai, Xin Yao

Bearing this in mind, this paper develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner.

Decision Making Learning-To-Rank

An Efficient Multi-Indicator and Many-Objective Optimization Algorithm based on Two-Archive

no code implementations14 Jan 2022 ZiMing Wang, Xin Yao

We also analyzed how normalizing affected the indicator-based algorithm and observed that the normalized $I_{\epsilon+}$ indicator is better at finding extreme solutions and can reduce the influence of each objective's different extent of contribution to the indicator due to its different scope.

Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network

no code implementations12 Jan 2022 Gan Ruan, Leandro L. Minku, Zhao Xu, Xin Yao

However, the existing problem formulation to solve this resource allocation problem is unsuitable as it defines the capacity utility of resource in an inappropriate way and tends to cause much delay.

TRIP: Refining Image-to-Image Translation via Rival Preferences

no code implementations26 Nov 2021 Yinghua Yao, Yuangang Pan, Ivor W. Tsang, Xin Yao

In particular, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth subtle changes with respect to the interested attributes; and a ranker that ranks rival preferences consisting of the input image and the desired image.

Image-to-Image Translation Translation

Hybrid Beamforming for RIS-Aided Communications: Fitness Landscape Analysis and Niching Genetic Algorithm

no code implementations19 Sep 2021 Bai Yan, Qi Zhao, Jin Zhang, J. Andrew Zhang, Xin Yao

To investigate the number and distribution of local optima, we conduct a fitness landscape analysis on the sum rate maximization problems.

Lifelong Computing

no code implementations19 Aug 2021 Danny Weyns, Thomas Bäck, Renè Vidal, Xin Yao, Ahmed Nabil Belbachir

When detecting anomalies, novelties, new goals or constraints, a lifelong computing system activates an evolutionary self-learning engine that runs online experiments to determine how the computing-learning system needs to evolve to deal with the changes, thereby changing its architecture and integrating new computing elements from computing warehouses as needed.

Self-Learning

Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark

1 code implementation23 Jul 2021 Mohammad Nabi Omidvar, Danial Yazdani, Juergen Branke, XiaoDong Li, Shengxiang Yang, Xin Yao

This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.

Differential-Critic GAN: Generating What You Want by a Cue of Preferences

no code implementations14 Jul 2021 Yinghua Yao, Yuangang Pan, Ivor W. Tsang, Xin Yao

This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property, which generates desired data that meets user's expectations and can assist in designing biological products with desired properties.

Multiobjective Bilevel Evolutionary Approach for Off-Grid Direction-of-Arrival Estimation

no code implementations14 Jun 2021 Bai Yan, Qi Zhao, Jin Zhang, J. Andrew Zhang, Xin Yao

We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation.

Direction of Arrival Estimation

Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order

no code implementations14 Jun 2021 Bai Yan, Qi Zhao, Jin Zhang, J. Andrew Zhang, Xin Yao

To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order by means of the atomic $l_0$ norm.

Multiobjective Optimization

Context-Based Soft Actor Critic for Environments with Non-stationary Dynamics

1 code implementation7 May 2021 Yuan Pu, Shaochen Wang, Xin Yao, Bin Li

The performance of deep reinforcement learning methods prone to degenerate when applied to environments with non-stationary dynamics.

Continuous Control

A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems

1 code implementation14 Apr 2021 Hao Tong, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao

The few existing studies are limited by the dynamic scenarios considered, and by overly complicated algorithms that are unable to benefit from the wealth of contributions provided by the existing CARP literature.

When Non-Elitism Meets Time-Linkage Problems

no code implementations14 Apr 2021 Weijie Zheng, Qiaozhi Zhang, Huanhuan Chen, Xin Yao

However, only two elitist algorithms (1+1)EA and ($\mu$+1)EA are analyzed, and it is unknown whether the non-elitism mechanism could help to escape the local optima existed in OneMax$_{(0, 1^n)}$.

Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows

1 code implementation12 Nov 2020 Shengcai Liu, Ke Tang, Xin Yao

The Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW) has attracted much research interest in the last decade, due to its wide application in modern logistics.

Reinforcement Learning with Dual-Observation for General Video Game Playing

1 code implementation11 Nov 2020 Chengpeng Hu, Ziqi Wang, Tianye Shu, Hao Tong, Julian Togelius, Xin Yao, Jialin Liu

Our proposed technique is implemented with three state-of-the-art reinforcement learning algorithms and tested on the game set of the 2020 General Video Game AI Learning Competition.

Decision Making reinforcement-learning

Few-shots Parallel Algorithm Portfolio Construction via Co-evolution

no code implementations1 Jul 2020 Ke Tang, Shengcai Liu, Peng Yang, Xin Yao

In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of training problem instances, which is often referred to as PAP construction.

Traveling Salesman Problem

A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem

no code implementations27 Jun 2020 Han Zhang, Jialin Liu, Xin Yao

The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics.

Decision Making

Continual Local Training for Better Initialization of Federated Models

2 code implementations26 May 2020 Xin Yao, Lifeng Sun

Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy concerns.

Federated Learning

Knee Point Identification Based on Trade-Off Utility

1 code implementation23 May 2020 Ke Li, Haifeng Nie, Huifu Gao, Xin Yao

Knee points, characterised as their smallest trade-off loss at all objectives, are attractive to decision makers in multi-criterion decision-making.

Decision Making

A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros

2 code implementations13 May 2020 Tianye Shu, Ziqi Wang, Jialin Liu, Xin Yao

However, defective levels with illegal patterns may be generated due to the violation of constraints for level design.

Analysis of Evolutionary Algorithms on Fitness Function with Time-linkage Property

no code implementations26 Apr 2020 Weijie Zheng, Huanhuan Chen, Xin Yao

In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current solution as well as the historical solutions.

How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

1 code implementation20 Feb 2020 Miqing Li, Tao Chen, Xin Yao

We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.

Multiobjective Optimization

Dynamic Multi-objective Optimization of the Travelling Thief Problem

no code implementations7 Feb 2020 Daniel Herring, Michael Kirley, Xin Yao

A combined approach that mixes solution generation methods to provide a composite population in response to dynamic changes provides improved performance in some instances for the different dynamic TTP formulations.

Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline

no code implementations20 Jan 2020 Tao Chen, Rami Bahsoon, Xin Yao

To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process.

Explicit Planning for Efficient Exploration in Reinforcement Learning

no code implementations NeurIPS 2019 Liangpeng Zhang, Ke Tang, Xin Yao

We argue that explicit planning for exploration can help alleviate such a problem, and propose a Value Iteration for Exploration Cost (VIEC) algorithm which computes the optimal exploration scheme by solving an augmented MDP.

Efficient Exploration reinforcement-learning

Adaptive Initialization Method for K-means Algorithm

no code implementations27 Nov 2019 Jie Yang, Yu-Kai Wang, Xin Yao, Chin-Teng Lin

(c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets.

On Performance Estimation in Automatic Algorithm Configuration

no code implementations19 Nov 2019 Shengcai Liu, Ke Tang, Yunwen Lei, Xin Yao

Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress.

Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning

no code implementations24 Oct 2019 Xin Yao, Tianchi Huang, Chenglei Wu, Rui-Xiao Zhang, Lifeng Sun

Extensive experiments in several typical lifelong learning scenarios demonstrate that our method outperforms the state-of-the-art methods in both accuracies on new tasks and performance preservation on old tasks.

Continual Learning Knowledge Distillation

Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating

no code implementations18 Oct 2019 Xin Yao, Tianchi Huang, Rui-Xiao Zhang, Ruiyu Li, Lifeng Sun

Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices.

Federated Learning

Parallel Exploration via Negatively Correlated Search

no code implementations16 Oct 2019 Peng Yang, Qi Yang, Ke Tang, Xin Yao

Empirical results show that the significant advantages of NCS over the compared state-of-the-art methods can be highly owed to the effective parallel exploration ability.

Atari Games reinforcement-learning

Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multi-Objective Optimisation Using Reference Points

no code implementations30 Sep 2019 Ke Li, Min-Hui Liao, Kalyanmoy Deb, Geyong Min, Xin Yao

The ultimate goal of multi-objective optimisation is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple conflicting criteria.

Decision Making

MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS

no code implementations25 Sep 2019 Tao Zheng, Ivor Tsang, Xin Yao

We propose an extendable and end-to-end deep representation approach for metric learning on multi-label data set that is based on neural networks able to operate on feature data or directly on raw image data.

Metric Learning Multi-Label Learning +1

Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs

2 code implementations16 Aug 2019 Xin Yao, Tianchi Huang, Chenglei Wu, Rui-Xiao Zhang, Lifeng Sun

Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices.

Federated Learning

Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning

1 code implementation6 Aug 2019 Tianchi Huang, Chao Zhou, Rui-Xiao Zhang, Chenglei Wu, Xin Yao, Lifeng Sun

Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required.

Imitation Learning

Competitive Coevolution as an Adversarial Approach to Dynamic Optimization

no code implementations31 Jul 2019 Xiaofen Lu, Ke Tang, Stefan Menzel, Xin Yao

In this paper, a new framework of employing EAs in the context of dynamic optimization is explored.

A Simple Yet Effective Approach to Robust Optimization Over Time

1 code implementation22 Jul 2019 Lukáš Adam, Xin Yao

Robust optimization over time (ROOT) refers to an optimization problem where its performance is evaluated over a period of future time.

Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms

no code implementations22 Apr 2019 Hao Tong, Jialin Liu, Xin Yao

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs).

Learning Topological Representation for Networks via Hierarchical Sampling

1 code implementation15 Feb 2019 Guoji Fu, Chengbin Hou, Xin Yao

To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network.

Link Prediction Representation Learning

Representation Learning for Heterogeneous Information Networks via Embedding Events

1 code implementation29 Jan 2019 Guoji Fu, Bo Yuan, Qiqi Duan, Xin Yao

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.

Link Prediction Node Classification +1

Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems

1 code implementation17 Jan 2019 Hao Tong, Changwu Huang, Jialin Liu, Xin Yao

A performance selector is designed to switch the search dynamically and automatically between the global and local search stages.

A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization

no code implementations6 Dec 2018 Peng Yang, Ke Tang, Xin Yao

Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas.

Analysis of Noisy Evolutionary Optimization When Sampling Fails

no code implementations11 Oct 2018 Chao Qian, Chao Bian, Yang Yu, Ke Tang, Xin Yao

In this paper, we first investigate the effect of sample size from a theoretical perspective.

Multiobjective Test Problems with Degenerate Pareto Fronts

no code implementations7 Jun 2018 Liangli Zhen, Miqing Li, Ran Cheng, Dezhong Peng, Xin Yao

We abstract three generic characteristics of degenerate problems, and on the basis of these characteristics we present a set of test problems, in order to support the investigation of multiobjective search algorithms on problems with redundant objectives.

Multiobjective Optimization

Automatic Construction of Parallel Portfolios via Explicit Instance Grouping

no code implementations17 Apr 2018 Shengcai Liu, Ke Tang, Xin Yao

Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems.

Evolutionary Generative Adversarial Networks

3 code implementations1 Mar 2018 Chaoyue Wang, Chang Xu, Xin Yao, DaCheng Tao

In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance.

Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions

no code implementations2 Jan 2018 Ke Li, Renzhi Chen, Dragan Savic, Xin Yao

In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i. e., a set of reference points that are biased toward to the ROI.

Decision Making

Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

no code implementations21 Nov 2017 Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao

When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously.

Maximizing Non-monotone/Non-submodular Functions by Multi-objective Evolutionary Algorithms

no code implementations20 Nov 2017 Chao Qian, Yang Yu, Ke Tang, Xin Yao, Zhi-Hua Zhou

To provide a general theoretical explanation of the behavior of EAs, it is desirable to study the performance of EAs on a general class of combinatorial optimization problems.

Combinatorial Optimization

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-based Evolutionary Multi-Objective Optimisation

no code implementations8 Sep 2017 Miqing Li, Xin Yao

A set of weights distributed uniformly in a simplex often lead to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may fail on other Pareto front shapes.

Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)

no code implementations28 Jul 2017 Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao

It provides a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning, concept drift, and the combined issues of class imbalance and concept drift.

Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results

no code implementations12 Jun 2017 Bingshui Da, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Zexuan Zhu, Chuan-Kang Ting, Ke Tang, Xin Yao

In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously.

Kernel Truncated Regression Representation for Robust Subspace Clustering

no code implementations15 May 2017 Liangli Zhen, Dezhong Peng, Wei Wang, Xin Yao

Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces.

How to Read Many-Objective Solution Sets in Parallel Coordinates

no code implementations30 Apr 2017 Miqing Li, Liangli Zhen, Xin Yao

In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives.

Experience-based Optimization: A Coevolutionary Approach

no code implementations29 Mar 2017 Shengcai Liu, Ke Tang, Xin Yao

The idea behind LiangYi is to promote the population-based solver by training it (with the training module) to improve its performance on those instances (discovered by the sampling module) on which it performs badly, while keeping the good performances obtained by it on previous instances.

A Systematic Study of Online Class Imbalance Learning with Concept Drift

no code implementations20 Mar 2017 Shuo Wang, Leandro L. Minku, Xin Yao

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift.

online learning

Concept Drift Adaptation by Exploiting Historical Knowledge

no code implementations12 Feb 2017 Yu Sun, Ke Tang, Zexuan Zhu, Xin Yao

Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data.

Ensemble Learning Incremental Learning +1

Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization

no code implementations1 Feb 2017 Miqing Li, Xin Yao

In this paper, we propose a quality measure, called dominance move (DoM), to compare solution sets generated by multiobjective optimizers.

Decision Making Multiobjective Optimization

Integration of Preferences in Decomposition Multi-Objective Optimization

no code implementations20 Jan 2017 Ke Li, Kalyanmoy Deb, Xin Yao

Extensive experiments, both proof-of-principle and on a variety of problems with 3 to 10 objectives, fully demonstrate the effectiveness of our proposed method for approximating the preferred solutions in the region of interest.

Decision Making

Robust Online Time Series Prediction with Recurrent Neural Networks

no code implementations26 Dec 2016 Tian Guo, Zhao Xu, Xin Yao, Haifeng Chen, Karl Aberer, Koichi Funaya

Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare.

online learning Time Series +2

Success Probability of Exploration: a Concrete Analysis of Learning Efficiency

no code implementations2 Dec 2016 Liangpeng Zhang, Ke Tang, Xin Yao

We then provide empirical results to verify our approach, and demonstrate how the success probability of exploration can be used to analyse and predict the behaviours and possible outcomes of exploration, which are the keys to the answer of the important questions of exploration.

Probabilistic Feature Selection and Classification Vector Machine

no code implementations18 Sep 2016 Bingbing Jiang, Chang Li, Maarten de Rijke, Xin Yao, Huanhuan Chen

The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.

Classification General Classification

Dynamic Multi-Objectives Optimization with a Changing Number of Objectives

no code implementations23 Aug 2016 Renzhi Chen, Ke Li, Xin Yao

Existing studies on dynamic multi-objective optimization focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature.

High-dimensional Black-box Optimization via Divide and Approximate Conquer

no code implementations11 Mar 2016 Peng Yang, Ke Tang, Xin Yao

Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems.

Negatively Correlated Search

no code implementations20 Apr 2015 Ke Tang, Peng Yang, Xin Yao

This paper presents a new EA, namely Negatively Correlated Search (NCS), which maintains multiple individual search processes in parallel and models the search behaviors of individual search processes as probability distributions.

A Unified Markov Chain Approach to Analysing Randomised Search Heuristics

no code implementations9 Dec 2013 Jun He, Feidun He, Xin Yao

The convergence, convergence rate and expected hitting time play fundamental roles in the analysis of randomised search heuristics.

Average Drift Analysis and Population Scalability

no code implementations14 Aug 2013 Jun He, Xin Yao

Population scalability is the ratio of the expected hitting time between a benchmark algorithm and an algorithm using a larger population size.

On the Easiest and Hardest Fitness Functions

no code implementations28 Mar 2012 Jun He, Tianshi Chen, Xin Yao

The aim of this paper is to answer the following research questions: Given a fitness function class, which functions are the easiest with respect to an evolutionary algorithm?

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