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@PasaOpasen
PasaOpasen committed Apr 14, 2024
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139 changes: 4 additions & 135 deletions README.md
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Expand Up @@ -55,7 +55,6 @@ https://pasaopasen.github.io/geneticalgorithm2/
- [Genetic algorithm's parameters](#genetic-algorithms-parameters)
- [AlgorithmParams object](#algorithmparams-object)
- [Parameters of algorithm](#parameters-of-algorithm)
- [**Count parameters**](#count-parameters)
- [**Crossover**](#crossover)
- [**Mutation**](#mutation)
- [**Selection**](#selection)
Expand Down Expand Up @@ -520,156 +519,26 @@ print(result.last_generation)

## Constructor parameters

* **function** (`Callable[[np.ndarray], float]`) - the given objective function to be minimized
NOTE: This implementation minimizes the given objective function. (For maximization multiply function by a negative sign: the absolute value of the output would be the actual objective function)

* **dimension** (`int`) - the number of decision variables

* **variable_type** (`Union[str, Sequence[str]]`) - 'bool' if all variables are Boolean; 'int' if all variables are integer; and 'real' if all variables are real value or continuous. For mixed types use sequence of string of type for each variable

* **variable_boundaries** (`Optional[Union[np.ndarray, Sequence[Tuple[float, float]]]]`) - Default None; leave it None if variable_type is 'bool'; otherwise provide an sequence of tuples of length two as boundaries for each variable; the length of the array must be equal dimension.
For example, `np.array([[0,100],[0,200]])` or `[(0, 100), (0, 200)]` determines lower boundary 0 and upper boundary 100 for first and upper boundary 200 for second variable where dimension is 2.

* **function_timeout** (`float`) - if the given function does not provide
output before function_timeout (unit is seconds) the algorithm raise error.
For example, when there is an infinite loop in the given function. `None` means disabling

* **algorithm_parameters** (`Union[AlgorithmParams, Dict[str, Any]]`). Dictionary or AlgorithmParams object with fields:
* @ **max_num_iteration** (`int/None`) - stopping criteria of the genetic algorithm (GA)
* @ **population_size** (`int > 0`)
* @ **mutation_probability** (`float in [0,1]`)
* @ **mutation_discrete_probability** (`float in [0,1]` or `None`)
* @ **elit_ration** (`float in [0,1]`) - part of elit objects in population; if > 0, there always will be 1 elit object at least
* @ **parents_portion** (`float in [0,1]`) - part of parents from previous population to save in next population (including `elit_ration`)
* @ **crossover_type** (`Union[str, Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]]`) - Default is `uniform`.
* @ **mutation_type** (`Union[str, Callable[[float, float, float], float]]`) - Default is `uniform_by_center`
* @ **mutation_discrete_type** (`Union[str, Callable[[int, int, int], int]]`) - Default is `uniform_discrete`
* @ **selection_type** (`Union[str, Callable[[np.ndarray, int], np.ndarray]]`) - Default is `roulette`
* @ **max_iteration_without_improv** (`int/None`) - maximum number of successive iterations without improvement. If `None` it is ineffective
Have a look at https://pasaopasen.github.io/geneticalgorithm2/geneticalgorithm2/geneticalgorithm2.html#GeneticAlgorithm2.__init__

## Genetic algorithm's parameters

### AlgorithmParams object

The parameters of GA is defined as a dictionary or `AlgorithmParams` object:

```python

algorithm_param = AlgorithmParams(
max_num_iteration = None,
population_size = 100,
mutation_probability = 0.1,
mutation_discrete_probability = None,
elit_ratio = 0.01,
parents_portion = 0.3,
crossover_type = 'uniform',
mutation_type = 'uniform_by_center',
mutation_discrete_type = 'uniform_discrete',
selection_type = 'roulette',
max_iteration_without_improv = None
)


# old style with dictionary
# sometimes it's easier to use this style
# especially if u need to set only few params
algorithm_param = {
'max_num_iteration': None,
'population_size':100,
'mutation_probability': 0.1,
'mutation_discrete_probability': None,
'elit_ratio': 0.01,
'parents_portion': 0.3,
'crossover_type':'uniform',
'mutation_type': 'uniform_by_center',
'mutation_discrete_type': 'uniform_discrete',
'selection_type': 'roulette',
'max_iteration_without_improv':None
}

```
The parameters of GA is defined as a dictionary or `AlgorithmParams` object: https://pasaopasen.github.io/geneticalgorithm2/geneticalgorithm2/data_types/algorithm_params.html

To get actual default params use code:
To get the global default params use code:
```python
params = ga.default_params
```

To get actual parameters of existing model use code:
To get actual parameters of an existing model use code:
```python
params = model.param
```

An example of setting a new set of parameters for genetic algorithm and running `geneticalgorithm2` for our first simple example again:

```python
import numpy as np
from geneticalgorithm2 import GeneticAlgorithm2 as ga


def f(X):
return np.sum(X)


varbound = [(0, 10)] * 3

algorithm_param = {'max_num_iteration': 3000,
'population_size': 100,
'mutation_probability': 0.1,
'mutation_discrete_probability': None,
'elit_ratio': 0.01,
'parents_portion': 0.3,
'crossover_type': 'uniform',
'mutation_type': 'uniform_by_center',
'mutation_discrete_type': 'uniform_discrete',
'selection_type': 'roulette',
'max_iteration_without_improv': None}

model = ga(function=f,
dimension=3,
variable_type='real',
variable_boundaries=varbound,
algorithm_parameters=algorithm_param
)

model.run()
```
**Important**. U may use the small dictionary with only important parameters; other parameters will be default. It means the dictionary
```js
algorithm_param = {'max_num_iteration': 150,
'population_size':1000}
```
is equal to:
```js
algorithm_param = {'max_num_iteration': 150,
'population_size':1000,
'mutation_probability': 0.1,
'mutation_discrete_probability': None,
'elit_ratio': 0.01,
'parents_portion': 0.3,
'crossover_type':'uniform',
'mutation_type': 'uniform_by_center',
'mutation_discrete_type': 'uniform_discrete',
'selection_type': 'roulette',
'max_iteration_without_improv':None}
```

But it is better to use `AlgorithmParams` object instead of dictionaries.

### Parameters of algorithm

#### **Count parameters**

* **max_num_iteration**: The termination criterion of GA.
If this parameter's value is `None` the algorithm sets maximum number of iterations automatically as a function of the dimension, boundaries, and population size. The user may enter any number of iterations that they want. It is highly recommended that the user themselves determines the **max_num_iterations** and not to use `None`. Notice that **max_num_iteration** has been changed to 3000 (it was already `None`).

* **population_size**: determines the number of trial solutions in each iteration.

* **elit_ration**: determines the number of elites in the population. The default value is 0.01 (i.e. 1 percent). For example when population size is 100 and **elit_ratio** is 0.01 then there is one elite unit in the population. If this parameter is set to be zero then `geneticalgorithm2` implements a standard genetic algorithm instead of elitist GA. [See example](#standard-ga-vs-elitist-ga) of difference

* **parents_portion**: the portion of population filled by the members of the previous generation (aka parents); default is 0.3 (i.e. 30 percent of population)

* **max_iteration_without_improv**: if the algorithms does not improve the objective function over the number of successive iterations determined by this parameter, then GA stops and report the best found solution before the `max_num_iterations` to be met. The default value is `None`.

#### **Crossover**

* **crossover_type**: there are several options including `'one_point'`, `'two_point'`, `'uniform'`, `'segment'`, `'shuffle'` crossover functions; default is `'uniform'` crossover. U also can use crossover as functions from `Crossover` class:
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35 changes: 33 additions & 2 deletions geneticalgorithm2/data_types/algorithm_params.py
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Expand Up @@ -34,11 +34,31 @@ class AlgorithmParams(DictLikeGetSet):
"""Base optimization parameters container"""

max_num_iteration: Optional[int] = None
"""max iterations count of the algorithm"""
"""
max iterations count of the algorithm
If this parameter's value is `None` the algorithm sets maximum number of iterations automatically
as a function of the dimension, boundaries, and population size.
The user may enter any number of iterations that they want.
It is highly recommended that the user themselves determines
the `max_num_iterations` and not to use `None`
"""

max_iteration_without_improv: Optional[int] = None
"""max iteration without progress"""
"""
max iteration without progress
if the algorithms does not improve
the objective function over the number of successive iterations
determined by this parameter,
then GA stops and report the best found solution
before the `max_num_iterations` to be met
"""

population_size: int = 100
"""
determines the number of trial solutions in each iteration
"""

mutation_probability: float = 0.1
mutation_discrete_probability: Optional[float] = None
Expand All @@ -47,7 +67,18 @@ class AlgorithmParams(DictLikeGetSet):
crossover_probability: Optional[float] = None

elit_ratio: float = 0.04
"""
determines the number of elites in the population.
For example, when population size is 100 and `elit_ratio` is 0.01
then there is 4 elite units in the population.
If this parameter is set to be zero then `GeneticAlgorithm2` implements
a standard genetic algorithm instead of elitist GA
"""
parents_portion: float = 0.3
"""
the portion of population filled by the members of the previous generation (aka parents)
"""

crossover_type: Union[str, CrossoverFunc] = 'uniform'
mutation_type: Union[str, MutationFloatFunc] = 'uniform_by_center'
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