
Fewshots Parameter Tuning via Coevolution
Generalization, i.e., the ability of addressing problem instances that a...
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A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
The reliable facility location problem (RFLP) is an important research t...
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Continual Local Training for Better Initialization of Federated Models
Federated learning (FL) refers to the learning paradigm that trains mach...
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Knee Point Identification Based on TradeOff Utility
Knee points, characterised as their smallest tradeoff loss at all objec...
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A Novel CNetassisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
Applying latent variable evolution to game level design has become more ...
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Analysis of Evolutionary Algorithms on Fitness Function with Timelinkage Property
In realworld applications, many optimization problems have the timelin...
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How to Evaluate Solutions in Paretobased SearchBased Software Engineering? A Critical Review and Methodological Guidance
With modern requirements, there is an increasing tendancy of considering...
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Dynamic Multiobjective Optimization of the Travelling Thief Problem
Investigation of detailed and complex optimisation problem formulations ...
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Synergizing Domain Expertise with SelfAwareness in Software Systems: A Patternized Architecture Guideline
Architectural patterns provide a reusable architectural solution for com...
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Adaptive Initialization Method for Kmeans Algorithm
The Kmeans algorithm is a widely used clustering algorithm that offers ...
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On Performance Estimation in Automatic Algorithm Configuration
Over the last decade, research on automated parameter tuning, often refe...
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Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
Human beings are able to master a variety of knowledge and skills with o...
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Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
Federated Averaging (FedAvg) serves as the fundamental framework in Fede...
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Negatively Correlated Search as a Parallel Exploration Search Strategy
Parallel exploration is a key to a successful search. The recently propo...
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Does Preference Always Help? A Holistic Study on PreferenceBased Evolutionary MultiObjective Optimisation Using Reference Points
The ultimate goal of multiobjective optimisation is to help a decision ...
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Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
Federated learning (FL) enables ondevice training over distributed netw...
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Comyco: QualityAware Adaptive Video Streaming via Imitation Learning
Learningbased Adaptive Bit Rate (ABR) method, aiming to learn outstandi...
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Competitive Coevolution for Dynamic Constrained Optimisation
Dynamic constrained optimisation problems (DCOPs) widely exist in the re...
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A Simple Yet Effective Approach to Robust Optimization Over Time
Robust optimization over time (ROOT) refers to an optimization problem w...
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Algorithm Portfolio for Individualbased SurrogateAssisted Evolutionary Algorithms
Surrogateassisted evolutionary algorithms (SAEAs) are powerful optimisa...
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Learning Topological Representation for Networks via Hierarchical Sampling
The topological information is essential for studying the relationship b...
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Representation Learning for Heterogeneous Information Networks via Embedding Events
Network representation learning (NRL) has been widely used to help analy...
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Voronoibased Efficient Surrogateassisted Evolutionary Algorithm for Very Expensive Problems
Very expensive problems are very common in practical system that one fit...
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A Parallel DivideandConquer based Evolutionary Algorithm for Largescale Optimization
Largescale optimization problems that involve thousands of decision var...
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Tiyuntsong: A SelfPlay Reinforcement Learning Approach for ABR Video Streaming
Existing reinforcement learning(RL)based adaptive bitrate(ABR) approach...
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Analysis of Noisy Evolutionary Optimization When Sampling Fails
In noisy evolutionary optimization, sampling is a common strategy to dea...
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Multiobjective Test Problems with Degenerate Pareto Fronts
In multiobjective optimization, a set of scalable test problems with a v...
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Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Simultaneously utilizing several complementary solvers is a simple yet e...
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Evolutionary Generative Adversarial Networks
Generative adversarial networks (GAN) have been effective for learning g...
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Interactive Decomposition MultiObjective Optimization via Progressively Learned Value Functions
Decomposition has become an increasingly popular technique for evolution...
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TwoArchive Evolutionary Algorithm for Constrained MultiObjective Optimization
When solving constrained multiobjective optimization problems, an impor...
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Maximizing Nonmonotone/Nonsubmodular Functions by Multiobjective Evolutionary Algorithms
Evolutionary algorithms (EAs) are a kind of natureinspired generalpurp...
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What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decompositionbased Evolutionary MultiObjective Optimisation
The quality of solution sets generated by decompositionbased evolutiona...
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Evolutionary Multitasking for Singleobjective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
In this report, we suggest nine test problems for multitask singleobje...
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Kernel Truncated Regression Representation for Robust Subspace Clustering
Subspace clustering aims to group data points into multiple clusters of ...
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How to Read ManyObjective Solution Sets in Parallel Coordinates
Rapid development of evolutionary algorithms in handling manyobjective ...
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Experiencebased Optimization: A Coevolutionary Approach
This paper studies improving solvers based on their past solving experie...
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Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization
One of the most common approaches for multiobjective optimization is to ...
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Integration of Preferences in Decomposition MultiObjective Optimization
Most existing studies on evolutionary multiobjective optimization focus...
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Probabilistic Feature Selection and Classification Vector Machine
Sparse Bayesian learning is one of the stateof theart machine learnin...
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Dynamic MultiObjectives Optimization with a Changing Number of Objectives
Existing studies on dynamic multiobjective optimization focus on proble...
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Highdimensional Blackbox Optimization via Divide and Approximate Conquer
Divide and Conquer (DC) is conceptually well suited to highdimensional ...
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Negatively Correlated Search
Evolutionary Algorithms (EAs) have been shown to be powerful tools for c...
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A Unified Markov Chain Approach to Analysing Randomised Search Heuristics
The convergence, convergence rate and expected hitting time play fundame...
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Average Drift Analysis and Population Scalability
This paper aims to study how the population size affects the computation...
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Convex HullBased Multiobjective Genetic Programming for Maximizing ROC Performance
ROC is usually used to analyze the performance of classifiers in data mi...
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Learning in the Model Space for Fault Diagnosis
The emergence of large scaled sensor networks facilitates the collection...
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A Large Population Size Can Be Unhelpful in Evolutionary Algorithms
The utilization of populations is one of the most important features of ...
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On the Easiest and Hardest Fitness Functions
The hardness of fitness functions is an important research topic in the ...
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Scaling Up Estimation of Distribution Algorithms For Continuous Optimization
Since Estimation of Distribution Algorithms (EDA) were proposed, many at...
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Xin Yao
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Professor of Computer Science in the School of Computer Science at the University of Birmingham and the Director of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA)