pycellga.example package

Submodules

pycellga.example.example_alpha_cga module

class pycellga.example.example_alpha_cga.ExampleProblem

Bases: object

Example problem class to be minimized.

This class implements a simple sum of squares function with a global minimum value of 0, achieved when all elements of the chromosome are equal to 0.

__init__()

f(x)

Compute the objective function value.

This method implements the sum of squares function.

Parameters:

x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.

Returns:

The computed value of the function given the input x.

Return type:

float

pycellga.example.example_alpha_cga.run_alpha_cga_example()

Run the Alpha Cellular Genetic Algorithm (alpha_cga) using the optimizer module.

The alpha_cga is configured with a 5x5 grid, 100 generations, and a chromosome size of 10. The problem being solved is an instance of the ExampleProblem class, with real-valued genes, constrained by specified mins and maxs.

Returns:

A tuple containing the best solution chromosome and its corresponding value.

Return type:

tuple

pycellga.example.example_ccga module

class pycellga.example.example_ccga.ExampleProblem

Bases: object

Example problem class to be minimized.

This class implements a simple binary optimization problem, where the goal is to maximize the number of 1s.

__init__()

f(x)

Compute the objective function value.

This method implements a simple sum of binary values.

Parameters:

x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).

Returns:

The computed value of the function given the input x.

Return type:

int

pycellga.example.example_ccga.run_ccga_example()

Run the Compact Cellular Genetic Algorithm (ccga) using the optimizer module.

The ccga is configured with a 5x5 grid, 100 generations, and a chromosome size of 10. The problem being solved is an instance of the ExampleProblem class, with binary genes, constrained by specified mins and maxs.

Returns:

A tuple containing the best solution chromosome and its corresponding value.

Return type:

tuple

pycellga.example.example_cga module

class pycellga.example.example_cga.ExampleProblem

Bases: object

Example problem class to be minimized.

This class implements a simple sum of squares function with a global minimum value of 0, achieved when all elements of the chromosome are equal to 0.

__init__()

f(x)

Compute the objective function value.

This method implements the sum of squares function.

Parameters:

x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.

Returns:

The computed value of the function given the input x.

Return type:

float

pycellga.example.example_cga.run_cga_example()

Run the Cellular Genetic Algorithm (cga) using the optimizer module.

The cga is configured with a 5x5 grid, 100 generations, and a chromosome size of 5. The problem being solved is an instance of the ExampleProblem class, with real-valued genes, constrained by specified mins and maxs.

Returns:

A tuple containing the best solution chromosome and its corresponding value.

Return type:

tuple

pycellga.example.example_mcccga module

class pycellga.example.example_mcccga.RealProblem

Bases: object

Example problem class to be minimized.

This class implements a simple sum of squares function with a global minimum value of 0, achieved when all elements of the chromosome are equal to 0.

__init__()

f(x)

Compute the objective function value.

This method implements the sum of squares function.

Parameters:

x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.

Returns:

The computed value of the function given the input x.

Return type:

float

pycellga.example.example_mcccga.run_mcccga_example()

Run the Machine-Coded Compact Cellular Genetic Algorithm (mcccga) using the optimizer module.

The mcccga is configured with a 5x5 grid, 100 generations, and a chromosome size of 10. The problem being solved is an instance of the RealProblem class, with real genes, constrained by specified mins and maxs.

Returns:

A tuple containing the best solution chromosome and its corresponding value.

Return type:

tuple

pycellga.example.example_sync_cga module

class pycellga.example.example_sync_cga.ExampleProblem

Bases: object

Example problem class to be minimized.

This class implements a simple sum of squares function with a global minimum value of 0, achieved when all elements of the chromosome are equal to 0.

__init__()

f(x)

Compute the objective function value.

This method implements the sum of squares function.

Parameters:

x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.

Returns:

The computed value of the function given the input x.

Return type:

float

pycellga.example.example_sync_cga.run_sync_cga_example()

Run the Synchronous Cellular Genetic Algorithm (sync_cga) using the optimizer module.

The sync_cga is configured with a 5x5 grid, 100 generations, and a chromosome size of 5. The problem being solved is an instance of the ExampleProblem class, with real-valued genes, constrained by specified mins and maxs.

Returns:

A tuple containing the best solution chromosome and its corresponding value.

Return type:

tuple

Module contents