Search Results for author:
Found 16 papers, 2 papers with code
CausalBench: A Comprehensive Benchmark for Causal Learning Capability of Large Language Models
To address these challenges, this paper proposes a comprehensive benchmark, namely CausalBench, to evaluate the causality understanding capabilities of LLMs.
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
To the best of our knowledge, this work presents the first systematic evaluation of LLMs for numerical optimization, offering a progressive, wide-coverage, and behavioral analysis.
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
Large Language Models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence.
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.
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.
