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METApy
Home
Learning
Probabilistic
Hill Climbing
Simulated Annealing
Genetic Algorithm
Differential Evolution
Gender Firefly
Chaos map
Applications
Inverse problem
Finance problem
Structure Problem
Framework
metaheuristic_optimizer
Common Library functions
initial_population_01
initial_population_02
initial_pops
fit_value
best_values
check_interval_01
id_selection
agent_selection
mutation_01_hill_movement
mutation_02_chaos_movement
mutation_03_de_movement
mutation_04_de_movement
mutation_05_de_movement
mutation_06_de_movement
mutation_07_de_movement
Simulated Annealing functions
hill_climbing_01
simulated_annealing_01
start_temperature
Genetic Algorithm functions
genetic_algorithm_01
linear_crossover
blxalpha_crossover
heuristic_crossover
simulated_binary_crossover
arithmetic_crossover
laplace_crossover
uniform_crossover
binomial_crossover
single_point_crossover
multi_point_crossover
Benchmark
Mathematical Functions
sphere
rosenbrock
rastrigin
ackley
griewank
zakharov
easom
michalewicz
dixon_price
goldstein_price
powell
Statistical
Loss
loss_function_mse
loss_function_mae
loss_function_mape
loss_function_hubber
loss_function_rmse
loss_function_r2
loss_function_r2_adjusted
Release notes
PyPI repo
Learning
Probabilistic
This section presents an review of the optimization methods.
Table of contents
Hill Climbing
Simulated Annealing
Genetic Algorithm
Differential Evolution
Gender Firefly
Chaos map