Probabilistic Mutation Scheduling for EAs

Instead of a fixed mutation rate, dynamically adjusting mutation probability based on generation performance could improve long-term adaptability.

Look at the evolutionary algorithm (EA) implementation to understand the current mutation scheduling. Identify where mutation rates are defined and how generation performance is measured. The change involves designing a probabilistic model to adjust mutation rates dynamically, which requires understanding the fitness landscape and algorithm convergence.

Scritto dal modello di indicizzazione a partire dal testo della issue.