Method and system for optimizing performance of genetic algorithm in solving scheduling problems

This invention describes a computer method to make Genetic Algorithms (GAs) better at solving scheduling problems. It takes scheduling requirements, initializes the GA, and then evolves potential solutions. A key feature is its ability to automatically adjust the GA's internal settings, such as how it mutates or combines solutions, during runtime if it's not achieving good results within a set number of iterations, continuing until an optimal schedule is found.

Why it matters: The rapid evolution of AI frameworks and increased computational power since 2024 makes the development and deployment of complex, adaptive optimization systems, like this GA meta-optimizer, more practical and efficient.

Status: Under examinationA pending application. It has not been granted, and it has not been abandoned.
How hard to build: SpecializedSoftware logic, AI/optimization expertise

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