
Robust Dynamic Network Embedding via Ensembles
Dynamic Network Embedding (DNE) has recently attracted considerable atte...
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Multiobjective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
Evolutionary algorithms (EAs) are generalpurpose optimization algorithm...
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A New Knowledge Gradientbased Method for Constrained Bayesian Optimization
Blackbox problems are common in real life like structural design, drug ...
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A Survey on Neural Network Interpretability
Along with the great success of deep neural networks, there is also grow...
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Memetic Search for Vehicle Routing with Simultaneous PickupDelivery and Time Windows
The vehicle routing problem with simultaneous pickupdelivery and time w...
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Interpreting Deep Learning Model Using Rulebased Method
Deep learning models are favored in many research and industry areas and...
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Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search
Evolutionary algorithms (EAs) have been successfully applied to optimize...
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GloDyNE: Global Topology Preserving Dynamic Network Embedding
Learning lowdimensional topological representation of a network in dyna...
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Fewshots Parameter Tuning via Coevolution
Generalization, i.e., the ability of addressing problem instances that a...
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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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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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Competitive Coevolution for Dynamic Constrained Optimisation
Dynamic constrained optimisation problems (DCOPs) widely exist in the re...
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DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding
Learning topological representation of a network in dynamic environments...
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Running Time Analysis of the (1+1)EA for Robust Linear Optimization
Evolutionary algorithms (EAs) have found many successful realworld appl...
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Decision Making with Machine Learning and ROC Curves
The Receiver Operating Characteristic (ROC) curve is a representation of...
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Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions
Stochastic gradient descent (SGD) is a popular and efficient method with...
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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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Attributed Network Embedding for Incomplete Structure Information
An attributed network enriches a pure network by encoding a part of wide...
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Maximizing Monotone DRsubmodular Continuous Functions by Derivativefree Optimization
In this paper, we study the problem of monotone (weakly) DRsubmodular c...
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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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Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Simultaneously utilizing several complementary solvers is a simple yet e...
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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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Running Time Analysis of the (1+1)EA for OneMax and LeadingOnes under Bitwise Noise
In many realworld optimization problems, the objective function evaluat...
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Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization
In evolutionary algorithms, a preselection operator aims to select the p...
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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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Experiencebased Optimization: A Coevolutionary Approach
This paper studies improving solvers based on their past solving experie...
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An Adaptive Framework to Tune the Coordinate Systems in Evolutionary Algorithms
In the evolutionary computation research community, the performance of m...
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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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Relief RCNN : Utilizing Convolutional Features for Fast Object Detection
RCNN style methods are sorts of the stateoftheart object detection m...
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Negatively Correlated Search
Evolutionary Algorithms (EAs) have been shown to be powerful tools for c...
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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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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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The Impact of Mutation Rate on the Computation Time of Evolutionary Dynamic Optimization
Mutation has traditionally been regarded as an important operator in evo...
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Feature Selection for MAUCOriented Classification Systems
Feature selection is an important preprocessing step for many pattern c...
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