Search Results for author:
Found 22 papers, 10 papers with code
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
How to train a generalizable meta-policy by continually learning a sequence of tasks?
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer optimization framework, on non-separable functions.
A Survey on Automated Design of Metaheuristic Algorithms
In the survey, we first present the concept of automated design of metaheuristic algorithms and provide a taxonomy by abstracting the automated design process into four parts, i. e., design space, design strategies, performance evaluation strategies, and targeted problems.
AutoOptLib: A Library of Automatically Designing Metaheuristic Optimization Algorithms in MATLAB
Metaheuristic algorithms are widely-recognized solvers for challenging optimization problems with multi-modality, discretization, large-scale, multi-objectivity, etc.
Semi-decentralized Federated Ego Graph Learning for Recommendation
In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
In this paper, we present a pure-Python library called PyPop7 for black-box optimization (BBO).
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer
To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains.
Evolutionary Multiparty Distance Minimization
In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives.
Toward multi-target self-organizing pursuit in a partially observable Markov game
The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit.
Single-shot Embedding Dimension Search in Recommender System
In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems.
AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures
However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family.
AutoML for Deep Recommender Systems: A Survey
To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems.
Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments
This is very helpful for the decision makers, especially when facing changing environments.
Pareto Policy Pool for Model-based Offline Reinforcement Learning
Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so online RL methods can learn in an offline manner by solely interacting with the model.
Evolutionary Robust Clustering Over Time for Temporal Data
In many clustering scenes, data samples' attribute values change over time.
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection.
Robotic Brain Storm Optimization: A Multi-target Collaborative Searching Paradigm for Swarm Robotics
Swarm intelligence optimization algorithms can be adopted in swarm robotics for target searching tasks in a 2-D or 3-D space by treating the target signal strength as fitness values.
Attention-oriented Brain Storm Optimization for Multimodal Optimization Problems
Rather than converge to a single global optimum, the proposed method can guide the search procedure to converge to multiple "salient" solutions.
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning
To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can be potentially generalized to other BCI cases.
Cooperative coevolution of real predator robots and virtual robots in the pursuit domain
This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the (predator) robots, called CCPSO-R, where real and virtual robots coexist in an evolutionary algorithm (EA).
Robotics Multiagent Systems
Student's t Distribution based Estimation of Distribution Algorithms for Derivative-free Global Optimization
Observing that the Student's t distribution has heavier and longer tails than the Gaussian, which may be beneficial for exploring the solution space, we propose a novel EDA algorithm termed ESTDA, in which the Student's t distribution, rather than Gaussian, is employed.
Random Drift Particle Swarm Optimization
The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field.
