Algorithm
selection + crossover + mutation
Genetic Algorithm Optimization
Evolutionary optimization project implementing selection, crossover, mutation, and fitness-driven search.
PythonGenetic AlgorithmsOptimization
Use case
complex search-space optimization
Project Overview
Implemented a configurable genetic algorithm for search-space optimization and evaluated convergence behavior on benchmark problem setups.
Challenge
Classical optimization methods can struggle in large, non-convex search spaces.
Solution
Implemented an evolutionary optimization loop with fitness scoring and iterative population updates.
Results
Produced stable convergence behavior on benchmark tasks and demonstrated practical optimization engineering skills.