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Found 28 papers, 9 papers with code
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.
Towards Fault Tolerance in Multi-Agent Reinforcement Learning
Agent faults pose a significant threat to the performance of multi-agent reinforcement learning (MARL) algorithms, introducing two key challenges.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
GatedUniPose: A Novel Approach for Pose Estimation Combining UniRepLKNet and Gated Convolution
Pose estimation is a crucial task in computer vision, with wide applications in autonomous driving, human motion capture, and virtual reality.
GateAttentionPose: Enhancing Pose Estimation with Agent Attention and Improved Gated Convolutions
This paper introduces GateAttentionPose, an innovative approach that enhances the UniRepLKNet architecture for pose estimation tasks.
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.
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.
OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling
Furthermore, to alleviate the data scarcity for optimization problems, and to bridge the gap between open-source LLMs on a small scale (e. g., Llama-3-8b) and closed-source LLMs (e. g., GPT-4), we further propose a data synthesis method namely ReSocratic.
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.
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.
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.
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.
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
Large Language Models prompting, such as using in-context demonstrations, is a mainstream technique for invoking LLMs to perform high-performance and solid complex reasoning (e. g., mathematical reasoning, commonsense reasoning), and has the potential for further human-machine collaborative scientific findings.
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.
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.
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.
Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization
In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals.
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.
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions
MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups.
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications
To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically.
Multi-factorial Optimization for Large-scale Virtual Machine Placement in Cloud Computing
However, as growing demand for cloud services, the existing EAs fail to implement in large-scale virtual machine placement (LVMP) problem due to the high time complexity and poor scalability.
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.
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.
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.
