Implement genetic algorithm for optimizing continuous functions #12378
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added
require tests
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| # Initialize population | ||
| self.population = self.initialize_population() | ||
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| def initialize_population(self) -> list[np.ndarray]: |
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As there is no test file in this pull request nor any test function or class in the file genetic_algorithm/genetic_algorithm_optimization.py, please provide doctest for the function initialize_population
| for _ in range(self.population_size) | ||
| ] | ||
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| def fitness(self, individual: np.ndarray) -> float: |
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As there is no test file in this pull request nor any test function or class in the file genetic_algorithm/genetic_algorithm_optimization.py, please provide doctest for the function fitness
| value = float(self.function(*individual)) # Ensure fitness is a float | ||
| return value if self.maximize else -value # If minimizing, invert the fitness | ||
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| def select_parents( |
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As there is no test file in this pull request nor any test function or class in the file genetic_algorithm/genetic_algorithm_optimization.py, please provide doctest for the function select_parents
| ) | ||
| ) | ||
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| def evolve(self, verbose=True) -> np.ndarray: |
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As there is no test file in this pull request nor any test function or class in the file genetic_algorithm/genetic_algorithm_optimization.py, please provide doctest for the function evolve
Please provide type hint for the parameter: verbose
algorithms-keeper
bot
removed
require tests
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Describe your change:
Added a flexible genetic algorithm that allows users to define their own target functions for optimization.
Included features for population initialization, fitness evaluation, selection, crossover, and mutation.
Example function provided for minimizing f(x, y) = x^2 + y^2.
Configurable parameters for population size, mutation probability, and generations.
Add an algorithm? ✅ Yes
Fix a bug or typo in an existing algorithm? ❌ No
Add or change doctests? ❌ No
Documentation change? ❌ No
Checklist: