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Found 72 papers, 19 papers with code
The Vision of Self-Evolving Computing Systems
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
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
Bearing this in mind, this paper develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner.
An Efficient Multi-Indicator and Many-Objective Optimization Algorithm based on Two-Archive
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
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
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.
Hybrid Beamforming for RIS-Aided Communications: Fitness Landscape Analysis and Niching Genetic Algorithm
To investigate the number and distribution of local optima, we conduct a fitness landscape analysis on the sum rate maximization problems.
Lifelong Computing
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.
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
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
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
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.
Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order
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.
IEEE CEC 2022 Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark
This document describes the Generalized Moving Peaks Benchmark (GMPB) that generates continuous dynamic optimization problem instances.
Context-Based Soft Actor Critic for Environments with Non-stationary Dynamics
The performance of deep reinforcement learning methods prone to degenerate when applied to environments with non-stationary dynamics.
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
Deep reinforcement learning methods have shown great performance on many challenging cooperative multi-agent tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
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A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems
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
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
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
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.
Few-shots Parallel Algorithm Portfolio Construction via Co-evolution
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.
A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
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.
Continual Local Training for Better Initialization of Federated Models
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.
Knee Point Identification Based on Trade-Off Utility
Knee points, characterised as their smallest trade-off loss at all objectives, are attractive to decision makers in multi-criterion decision-making.
A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
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
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
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.
Dynamic Multi-objective Optimization of the Travelling Thief Problem
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
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
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.
Adaptive Initialization Method for K-means Algorithm
(c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets.
On Performance Estimation in Automatic Algorithm Configuration
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
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.
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices.
Parallel Exploration via Negatively Correlated Search
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.
Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multi-Objective Optimisation Using Reference Points
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.
MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS
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.
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
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.
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
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.
Competitive Coevolution as an Adversarial Approach to Dynamic Optimization
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
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
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs).
Learning Topological Representation for Networks via Hierarchical Sampling
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.
Ranked #1 on
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Representation Learning for Heterogeneous Information Networks via Embedding Events
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.
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Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems
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
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
In this paper, we first investigate the effect of sample size from a theoretical perspective.
Multiobjective Test Problems with Degenerate Pareto Fronts
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.
Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems.
Evolutionary Generative Adversarial Networks
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
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.
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning
Under/overestimation of state/action values are harmful for reinforcement learning agents.
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
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
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.
What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-based Evolutionary Multi-Objective Optimisation
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)
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
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
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
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
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
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift.
Concept Drift Adaptation by Exploiting Historical Knowledge
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.
Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization
In this paper, we propose a quality measure, called dominance move (DoM), to compare solution sets generated by multiobjective optimizers.
Integration of Preferences in Decomposition Multi-Objective Optimization
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.
Robust Online Time Series Prediction with Recurrent Neural Networks
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.
Success Probability of Exploration: a Concrete Analysis of Learning Efficiency
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
The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.
Dynamic Multi-Objectives Optimization with a Changing Number of Objectives
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
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
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
The convergence, convergence rate and expected hitting time play fundamental roles in the analysis of randomised search heuristics.
Average Drift Analysis and Population Scalability
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
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?
