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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
This section presents an review of the optimization methods and their applications.
Table of contents
Probabilistic
Applications