ggga¶
Run an example optimization benchmark function
ggga [OPTIONS] COMMAND [ARGS]...
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled. [default: 50]
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise. [default: 0.0]
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor. [default: gpr]
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs. [default: 7861]
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
easom¶
Easom: Flat function with single sharp minimum.
ggga easom [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
goldstein-price¶
Goldstein-Price: Asymetric function with single optimum.
ggga goldstein-price [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
himmelblau¶
Himmelblau’s function: Asymetric polynomial with 4 minima.
ggga himmelblau [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
onemax¶
One-Max function.
ggga onemax [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
-
-D
,
--dimensions
<dimensions>
¶ Number of parameters/dimensions. [default: 4]
onemax-log¶
One-Max function.
ggga onemax-log [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
-
-D
,
--dimensions
<dimensions>
¶ Number of parameters/dimensions. [default: 4]
rastrigin¶
Rastrigin Function: N-dimensional with many local minima.
ggga rastrigin [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
-
-D
,
--dimensions
<dimensions>
¶ Number of parameters/dimensions. [default: 2]
rosenbrock¶
Rosenbrock function: N-dimensional and asymetric.
ggga rosenbrock [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
-
-D
,
--dimensions
<dimensions>
¶ Number of parameters/dimensions. [default: 2]
sphere¶
Sphere function: N-dimensional, symmetric.
ggga sphere [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
-
-D
,
--dimensions
<dimensions>
¶ Number of parameters/dimensions. [default: 2]
trap¶
Like One-Max, but has misleading gradient.
ggga trap [OPTIONS]
Options
-
--interactive
,
--no-interactive
¶
Whether to display the generated plots.
-
--samples
<samples>
¶ How many evaluations should be sampled.
-
--logy
¶
Log-transform the objective function.
-
--noise
<noise>
¶ Standard deviation of test function noise.
-
--model
<model>
¶ The surrogate model implementation used for prediction. gpr: Gaussian Process Regression. knn: k-Nearest Neighbor.
-
--quiet
¶
Don’t display human-readable output during minimization.
-
--seed
<seed>
¶ Seed for reproducible runs.
-
-s
,
--strategy
<strategies>
¶ Which optimization strategy will be used. Can be ‘random’, ‘ggga’, or a YAML document describing the strategy.
-
--csv
<csv>
¶ Write evaluation results to a CSV file. Only use this when running a single strategy.
-
--style
<style>
¶ DualDependenceStyle for the plots.
-
-D
,
--dimensions
<dimensions>
¶ Number of parameters/dimensions. [default: 2]