A from-scratch exploration of genetic algorithms for optimization, focused on population-based search and fitness-driven candidate selection.
- chromosome / candidate representation
- population initialization
- fitness-function design
- selection and reproduction
- crossover and mutation
- iterative convergence
- trade-offs between exploration and exploitation
initial population
↓
fitness evaluation
↓
selection
↓
crossover + mutation
↓
new population
↓
repeat until stopping condition
- C#
- genetic algorithms
- optimization
Academic / educational project. The original implementation is preserved unchanged; this README documents the engineering context.
Keep future changes focused and merge validated work into main through pull requests.