In the rapidly evolving realm of genetics, the integration of artificial intelligence (AI) has ushered in new perspectives on therapeutic approaches and evolutionary processes. Traditional genetic ...
Abstract: In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can ...
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: 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 ...
Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability. In this study, we investigate the case of multi-attraction tourism planning in Qujing ...
Abstract: Problem transformation-based multiobjective evolutionary algorithms (MOEAs) face the risk of losing optimal solutions when transforming a large-scale multiobjective optimization problem into ...
Nearly a quarter (24%) of buy-side trading desks plan to implement internal AI technologies for trade execution in the next 12 months, with 15% already doing so, according to a new report from Crisil ...
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 ...
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