Can Large Language Models Solve Optimisation Problems?
A new study explores how GPT-3.5 can generate and debug optimisation models in MiniZinc, bringing us closer to automated problem-solving.
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AutoMH - When Computer Programs Learn to Write Better Computer Programs
How computer programs learned to create better computer programs all by themselves, like digital evolution happening right before our eyes
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The Aerial Acrobats: How Hummingbird Flight Patterns Inspire Revolutionary Optimization Algorithms That Solve Complex Problems
A groundbreaking bio-inspired algorithm captures the extraordinary maneuverability of hummingbirds—their translations, rotations, and complex turns—transforming these aerial acrobatics into powerful computational tools for solving optimization challenges that stump traditional methods.
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Digital Puzzle Masters: How Computer Programs Spent Six Days Solving the Ultimate Factory Organization Challenge with Mathematical Perfection
Scientists created systematic experiments that searched through billions of factory arrangements for six days straight to find the mathematically perfect way to organize machines and parts into three manufacturing cells, achieving optimal solutions for the first time.
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Digital Andean Condors Soaring Through Mathematical Landscapes: How South America's Master Gliders Taught Computers to Navigate Infinite Optimization Spaces
Scientists created computer programs that explore continuous mathematical landscapes using the same soaring strategies Andean condors use to search vast mountain territories, achieving near-perfect solutions with elegant simplicity.
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The Digital Rangers: How Artificial Intelligence Learned to Protect Virtual Ecosystems from Collapse
A revolutionary breakthrough in artificial intelligence demonstrates how autonomous multi-agent systems can prevent ecosystem collapse by dynamically regulating predator-prey populations, offering new insights for conservation and environmental management in an era of climate change.
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