Implement genetic algorithm for optimizing continuous functions #11670
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- 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.
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Hi can anyone guide me why it is failing everytime i run it? |
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remove extra spaces thats the problem |
Thanks i will try to remove it and then rerun the jobs. |
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@night-spring can you guide me what extra spaces I need to remove as i am checking my code and based on that I can't able to find any extra whitespace. If possible for you can you help me for resolving the error and for merging this pr. |
after end of every para of your code you may give two newlines. remove one. |
Para means functions right? Thank you for you advice I will change it and push the code today itself |
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@night-spring and @cclauss what this error means? And also this one |
just fix format using copilot or any ai |
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https://docs.astral.sh/ruff/rules/#isort-i unsorted-imports (I001)Derived from the isort linter. Fix is sometimes available. What it doesDe-duplicates, groups, and sorts imports based on the provided Why is this bad?Consistency is good. Use a common convention for imports to make your code Exampleimport pandas
import numpy as npUse instead: import numpy as np
import pandas |
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@cclauss Thanks for the help, i will try to resolve the error. Thank you once again for giving your time. |
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@cclauss i have used these for the imports and use the same order import numpy as np
import random
from concurrent.futures import ThreadPoolExecutor |
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PEP8 has been the Python style guide for a long time. https://peps.python.org/pep-0008/#imports
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| class GeneticAlgorithm: | ||
| def __init__( |
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Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:
| class GeneticAlgorithm: | ||
| def __init__( | ||
| self, | ||
| function, |
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Please provide type hint for the parameter: function
| def __init__( | ||
| self, | ||
| function, | ||
| bounds, |
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| self, | ||
| function, | ||
| bounds, | ||
| population_size, |
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Please provide type hint for the parameter: population_size
| function, | ||
| bounds, | ||
| population_size, | ||
| generations, |
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| return child1, child2 | ||
| return parent1, parent2 | ||
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| def mutate(self, individual): |
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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 mutate
Please provide type hint for the parameter: individual
| individual[i] = rng.uniform(self.bounds[i][0], self.bounds[i][1]) | ||
| return individual | ||
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| def evaluate_population(self): |
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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 evaluate_population
| executor.map(lambda ind: (ind, self.fitness(ind)), self.population) | ||
| ) | ||
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| def evolve(self): |
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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
| population_score = self.evaluate_population() | ||
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| # Check the best individual | ||
| best_individual = max(population_score, key=lambda x: x[1])[0] |
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| # Example target function for optimization | ||
| def target_function(x, y): |
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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 target_function
Please provide type hint for the parameter: x
Please provide descriptive name for the parameter: x
Please provide type hint for the parameter: y
Please provide descriptive name for the parameter: y
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@cclauss can you please check my code as it is failing the test cases and don't know how to resolve, if possible for you. Thank you. |
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There are three Details buttons to the right of the three failing tests below. Click on each, read the error messages, and try to fix the code so those errors disappear.
import random
from concurrent.futures import ThreadPoolExecutor
import numpy as npgenetic_algorithm/genetic_algorithm_optimization.py:50:89: E501 Line too long (109 > 88) >>> ga = GeneticAlgorithm(lambda x, y: x**2 + y**2,
... [(-10, 10), (-10, 10)], 10, 100, 0.1, 0.8, False) |
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Thank you @cclauss for your guidance, i will try to make necessary changes that you listed . Thank you once again for your time. |
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:
Fixes #11578