An Enhanced Genetic Algorithm Implementation with Improved Convergence

This is my custom implementation of an enhanced genetic algorithm, primarily designed to overcome premature convergence issues commonly encountered when solving optimization problems. The implementation introduces novel crossover operators (such as simulated binary crossover with adaptive parameters) and intelligent mutation operators (including polynomial mutation with dynamic mutation rates) to enhance solution space exploration. Additionally, I've implemented an improved selection strategy combining tournament selection with elitism preservation to maintain population diversity. The algorithm also features optimized parameter configurations including adaptive crossover probability (varying between 0.6-0.9 based on population diversity) and dynamic mutation rates (scaling from 0.001 to 0.1 during evolution). Key functions include fitness scaling mechanisms, diversity monitoring through Hamming distance calculations, and stagnation detection with restart strategies. These enhancements enable the genetic algorithm to more effectively locate global optima in complex problem domains, significantly improving both algorithmic performance (measured by convergence speed and solution quality) and practical applicability across various optimization scenarios.