The Classic Genetic Algorithm implementation.
- Tournament Selection
- Roulette Selection
- Single Point Crossover
- Multi Point Crossover
- Single Bit Flip Mutation
- Stopping criteria
| Parameter | Description |
|---|---|
| n_simulations | Number of extinctions trials |
| max_iterations | Number of generations per simulation |
| target_fitness | Main stopping criteria of fitness |
| chromosome_length | DNA storage size |
| population_size | Number of individuals per generation |
| n_selections | Elitism, how many individuals will have children |
| mutation_probability | P(mutation) for each creation |
| selection_strategy | Parent determination strategy. tournament/roulette |
| crossover_strategy | Criteria for how offsprings will be produced |
| mutation_strategy | Mutation Strategy. bit-flip |
- Replace 'fitness' function,
- Adjust parameters according to the problem's needs.