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Many optimization frameworks lack built-in support for automatic parameter tuning or for the construction of new algorithms within the same architecture. To address this, we propose PyFOT (Python Framework for Optimization and Tuning Algorithm Parameters), a flexible and extensible Python framework that integrates algorithm development, problem modeling, and parameter tuning within a unified environment. Within this architecture, tuning is treated as an optimization problem, allowing both built-in and custom algorithms to be used as parameter tuners. This work demonstrates PyFOT’s tuning capabilities by improving the parameters of a genetic algorithm across three benchmark problems. Future work includes adding multi-objective optimization support, integrating new algorithms and benchmark problems, and extending core features.
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