Base Genetic Algorithm

Example:


from bejoor.genetic import  ToyGeneticAlgorithm, BaseGeneticAlgorithm

def func(sol):
    return abs( (sol[0]**sol[3] + sol[1]**sol[3] - sol[2]**sol[3]))


solution_vector =  [{"type": "integer", "lower_bound": 1, "upper_bound": 500}] * 3 + \
                   [{"type": "integer", "lower_bound": 2, "upper_bound": 100}] + \
                   [{"type": "float", "lower_bound": 0, "upper_bound": 1}] * 2 + \
                   [{"type": "string", "possible_values": ["option1", "option2", "option3"]}]

bga = BaseGeneticAlgorithm(objective_function=func, solution_vector_size=7,
                           solution_vector=solution_vector, optimization_side="min", target_objective_upper_bound=5,
                           crossover_probability=0.9, mutation_probability=0.1,
                           elitism_rate=0.05, selection_strategy="tournament",
                           crossover_type="one-point", population_size=500, epochs=200)
bga.run()

print(bga.best_solution)
print(bga.best_objective_value)

Parameters:

objective_function: Objective function needs to be optimized.
solution_vector_size: Vector size of the candidate solutions.
solution_vector: A vector which determines the types of each variable in solution vectors.
optimization_side: Determines maximize or minimize the objective function.
target_objective_value: Target Objective value.
target_objective_lower_bound: Target Objective lower bound.
target_objective_upper_bound: Target Objective upper bound.
population_size: Number of individuals in the population.
epochs: Number of generations to run the algorithm.
crossover_probability: Probability of crossover.
mutation_probability: Probability of mutation.
elitism_rate: Percentage of the best individuals to preserve.
selection_strategy: Strategy for parent selection (e.g., 'tournament', 'roulette').
crossover_type: Type of crossover ('one-point', 'two-point', 'uniform').

BibTeX citation to the algorithm


@book{holland1992adaptation,
  title={Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence},
  author={Holland, John H},
  year={1992},
  publisher={MIT press}
}

Bejoor

Base Genetic Algorithm

Example:

Parameters:

BibTeX citation to the algorithm

More useful resources about the algorithm: