EvoToolkit is a Python library that leverages Large Language Models (LLMs) to evolve solutions for optimization problems. It combines the power of evolutionary algorithms with LLM-based solution ...

Abstract: Balancing optimality and robustness is the key to solving expensive robust multiobjective optimization problems (ExRMOPs) by evolutionary algorithms. However, existing studies usually design ...

ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...

School of Information Engineering, Qujing Normal University, Qujing, China. Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability. In this ...

Want your business to show up in Google’s AI-driven results? The same principles that help you rank in Google Search still matter – but AI introduces new dimensions of context, reputation, and ...

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...

Abstract: Problem transformation-based multiobjective evolutionary algorithms (MOEAs) face the risk of losing optimal solutions when transforming a large-scale multiobjective optimization problem into ...

A new evolutionary technique from Japan-based AI lab Sakana AI enables developers to augment the capabilities of AI models without costly training and fine-tuning processes. The technique, called ...

Large language models (LLMs) leverage unsupervised learning to capture statistical patterns within vast amounts of text data. At the core of these models lies the Transformer architecture, which ...

The urban low-altitude logistics network adopts a hub-and-spoke, multi-layered structure. Its nodes are waypoints mapped to corresponding altitude layers based on spoke nodes (delivery spots) and hub ...