Search Results for author:
Found 76 papers, 24 papers with code
A New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation
We apply SDMC to two algorithm types that are unsuitable for traditional methods, confirming its effectiveness in global convergence analysis.
QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model
Large language models (LLMs) face significant challenges in specialized biomedical tasks due to the inherent complexity of medical reasoning and the sensitive nature of clinical data.
A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization
Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization.
Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects
Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses.
scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis
Functional MRI (fMRI) and single-cell transcriptomics are pivotal in Alzheimer's disease (AD) research, each providing unique insights into neural function and molecular mechanisms.
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
In addition to its performance-oriented design, $\texttt{$\textbf{EvoRL}$}$ offers a comprehensive platform for EvoRL research, encompassing implementations of traditional RL algorithms (e. g., A2C, PPO, DDPG, TD3, SAC), Evolutionary Algorithms (e. g., CMA-ES, OpenES, ARS), and hybrid EvoRL paradigms such as Evolutionary-guided RL (e. g., ERL, CEM-RL) and Population-Based AutoRL (e. g., PBT).
ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-offs inherent among competing objectives.
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning
Finally, a MBLO algorithm is presented to solve this problem while achieving high adaptability effectively.
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization
Based on the evaluation results, we meticulously identify a set of core designs that enhance the generalization and learning effectiveness of MetaBBO.
Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet
In recent years, deep learning-based methods have significantly improved speech enhancement performance, but they often come with a high computational cost, which is prohibitive for a large number of edge devices, such as headsets and hearing aids.
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability.
Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
To facilitate EMTO's performance, various knowledge transfer models have been developed for specific optimization tasks.
PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
Our experimental results on a wide range of pattern recognition tasks demonstrate the superiority of PMSN.
Design Principle Transfer in Neural Architecture Search via Large Language Models
In LAPT, LLM is applied to automatically reason the design principles from a set of given architectures, and then a principle adaptation method is applied to refine these principles progressively based on the new search results.
Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization
Expensive optimization problems (EOPs) have attracted increasing research attention over the decades due to their ubiquity in a variety of practical applications.
Learning to Transfer for Evolutionary Multitasking
However, current approaches in implicit EMT face challenges in adaptability, due to the use of a limited number of evolution operators and insufficient utilization of evolutionary states for performing KT.
Autonomous Multi-Objective Optimization Using Large Language Model
Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives.
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models
Providing explainable molecular property predictions is critical for many scientific domains, such as drug discovery and material science.
Fast 3D Molecule Generation via Unified Geometric Optimal Transport
This paper proposes a new 3D molecule generation framework, called GOAT, for fast and effective 3D molecule generation based on the flow-matching optimal transport objective.
Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features.
Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization
Solution's quality is assessed based on their constraint violations and objective-based performance.
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples.
CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals the underlying data distribution.
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains.
Diffusion-Driven Domain Adaptation for Generating 3D Molecules
These structure variations are encoded with an equivariant encoder and treated as domain supervisors to control denoising.
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems
Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges.
Scaling Supervised Local Learning with Augmented Auxiliary Networks
AugLocal constructs each hidden layer's auxiliary network by uniformly selecting a small subset of layers from its subsequent network layers to enhance their synergy.
Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals.
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
As the first comprehensive review focused on the EA research in the era of LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs.
Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking
A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions during the search process.
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
The high-dimensional algorithm representation extracted by LLM, after undergoing a feature selection module, is combined with the problem representation and passed to the similarity calculation module.
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs.
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely unexplored.
Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction
We demonstrate that textual descriptions alone can effectively serve as cues for extraction, thus addressing privacy concerns and reducing dependency on voiceprints.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
Long Short-term Memory with Two-Compartment Spiking Neuron
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer
Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics.
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience.
A Scalable Test Problem Generator for Sequential Transfer Optimization
Lastly, a benchmark suite with 12 STO problems featured by a variety of customized similarity relationships is developed using the proposed generator.
A Survey on Distributed Evolutionary Computation
Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment.
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach
To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations.
EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation
Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence.
Differentiable Search of Accurate and Robust Architectures
Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense.
An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection
Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer.
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.
Architecture Augmentation for Performance Predictor Based on Graph Isomorphism
Performance predictors can greatly alleviate the prohibitive cost of NAS by directly predicting the performance of DNNs.
A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs.
Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism
However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm.
Crowd Counting in the Frequency Domain
By transforming the density map into the frequency domain and using the nice properties of the characteristic function, we propose a novel method that is simple, effective, and efficient.
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization
Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others.
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model
At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space.
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.
Multi-Space Evolutionary Search for Large-Scale Optimization
This paper thus proposes a new search paradigm, namely the multi-space evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems.
Concept drift-tolerant transfer learning in dynamic environments.
Then, a domain-wise weighted ensemble is introduced to combine the source and target models to select useful knowledge of each domain.
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains
Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments.
A Survey on Evolutionary Neural Architecture Search
Deep Neural Networks (DNNs) have achieved great success in many applications.
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.
Synaptic Learning with Augmented Spikes
Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones.
Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes
We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets.
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing.
Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study
Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Online Bagging for Anytime Transfer Learning
In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially.
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments.
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.
Solving dynamic multi-objective optimization problems via support vector machine
The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)
The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
Our framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout.
Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters.
A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring
For fault diagnosis, a cost-sensitive deep belief network (namely ECS-DBN) is applied to deal with the imbalanced data problem for tool state estimation.
A Cost-Sensitive Deep Belief Network for Imbalanced Classification
Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
