# genetic_algorithm_driver.py¶

Driver for a simple genetic algorithm.

This is the Simple Genetic Algorithm implementation based on 2009 AAE550: MDO Lecture notes of Prof. William A. Crossley.

This basic GA algorithm is compartmentalized into the GeneticAlgorithm class so that it can be used in more complicated driver.

The following reference is only for the automatic population sizing: Williams E.A., Crossley W.A. (1998) Empirically-Derived Population Size and Mutation Rate Guidelines for a Genetic Algorithm with Uniform Crossover. In: Chawdhry P.K., Roy R., Pant R.K. (eds) Soft Computing in Engineering Design and Manufacturing. Springer, London.

The following reference is only for the penalty function: Smith, A. E., Coit, D. W. (1995) Penalty functions. In: Handbook of Evolutionary Computation, 97(1).

The following reference is only for weighted sum multi-objective optimization: Sobieszczanski-Sobieski, J., Morris, A. J., van Tooren, M. J. L. (2015) Multidisciplinary Design Optimization Supported by Knowledge Based Engineering. John Wiley & Sons, Ltd.

class openmdao.drivers.genetic_algorithm_driver.GeneticAlgorithm(objfun, comm=None, model_mpi=None)[source]

Bases: object

Simple Genetic Algorithm.

This is the Simple Genetic Algorithm implementation based on 2009 AAE550: MDO Lecture notes of Prof. William A. Crossley. It can be used standalone or as part of the OpenMDAO Driver.

Parameters
objfunfunction

Objective callback function.

commMPI communicator or None

The MPI communicator that will be used objective evaluation for each generation.

model_mpiNone or tuple

If the model in objfun is also parallel, then this will contain a tuple with the the total number of population points to evaluate concurrently, and the color of the point to evaluate on this rank.

Attributes
commMPI communicator or None

The MPI communicator that will be used objective evaluation for each generation.

elitebool

Elitism flag.

gray_codebool

Gray code binary representation flag.

cross_bitsbool

Crossover swaps bits instead of tails flag. Swapping bits is similar to mutation, so when used Pc should be increased and Pm reduced.

lchromint

Chromosome length.

model_mpiNone or tuple

If the model in objfun is also parallel, then this will contain a tuple with the the total number of population points to evaluate concurrently, and the color of the point to evaluate on this rank.

nobjint

Number of objectives.

npopint

Population size.

objfunfunction

Objective function callback.

__init__(objfun, comm=None, model_mpi=None)[source]

Initialize genetic algorithm object.

crossover(old_gen, Pc)[source]

Apply crossover to the current generation.

Crossover swaps tails (k-point crossover) of two adjacent genes.

Parameters
old_genndarray

Points in current generation.

Pcfloat

Probability of crossover.

Returns
ndarray

Current generation with crossovers applied.

decode(gen, vlb, vub, bits)[source]

Decode from binary array to real value array.

Parameters
genndarray

Population of points, encoded.

vlbndarray

Lower bound array.

vubndarray

Upper bound array.

bitsndarray(dtype=np.int)

Number of bits for decoding.

Returns
ndarray

Decoded design variable values.

encode(x, vlb, vub, bits)[source]

Encode array of real values to array of binary arrays.

The array of arrays represents a single population member.

Parameters
xndarray

Design variable values.

vlbndarray

Lower bound array.

vubndarray

Upper bound array.

bitsndarray(dtype=np.int)

Number of bits for decoding.

Returns
ndarray

Single population member, encoded.

eval_pareto(x, obj, x_nd, obj_nd)[source]

Produce a set of non dominated designs.

Parameters
xndarray

Design points from new generation.

objndarray

Objective values from new generation.

x_ndndarray

Non dominated design points from previous pareto evaluation.

obj_ndndarray

Non dominated objective values from previous pareto evaluation.

Returns
ndarray

Nondominated design points.

ndarray

Objective at nondominated design points.

execute_ga(x0, vlb, vub, vob, bits, pop_size, max_gen, random_state, Pm=None, Pc=0.5)[source]

Perform the genetic algorithm.

Parameters
x0ndarray

Initial design values.

vlbndarray

Lower bounds array.

vubndarray

Upper bounds array. This includes over-allocation so that every point falls on an integer value.

vobndarray

Outer bounds array. This is purely for bounds check.

bitsndarray

Number of bits to encode the design space for each element of the design vector.

pop_sizeint

Number of points in the population.

max_genint

Number of generations to run the GA.

random_statenp.random.RandomState, int

Random state (or seed-number) which controls the seed and random draws.

Pmfloat or None

Mutation rate.

Pcfloat

Crossover rate.

Returns
ndarray

Best design point.

float

Objective value at best design point.

int

Number of successful function evaluations.

static from_gray(g)[source]

Convert a Gray coded binary array to normal binary coding.

The input and output arrays represent a single population member.

Parameters
gbinary array

Gray coded binary array, e.g. np.array([0, 0, 1, 1]).

Returns
ndarray

Binary array using normal coding, e.g. np.array([0, 0, 1, 0]).

mutate(current_gen, Pm)[source]

Apply mutations to the current generation.

A mutation flips the state of the gene from 0 to 1 or 1 to 0.

Parameters
current_genndarray

Points in current generation.

Pmfloat

Probability of mutation.

Returns
ndarray

Current generation with mutations applied.

shuffle(old_gen)[source]

Shuffle (reorder) the points in the population.

Used in tournament selection.

Parameters
old_genndarray

Old population.

Returns
ndarray

New shuffled population.

ndarray(dtype=np.int)

Index array that maps the shuffle from old to new.

static to_gray(g)[source]

Convert a binary array representing a single population member to Gray code.

Parameters
gbinary array

Normal binary array, e.g. np.array([0, 0, 1, 0]).

Returns
ndarray

Binary array using Gray code, e.g. np.array([0, 0, 1, 1]).

tournament(old_gen, fitness)[source]

Apply tournament selection and keep the best points.

Parameters
old_genndarray

Points in current generation.

fitnessndarray

Objective value of each point.

Returns
ndarray

New generation with best points.

tournament_multi_obj(old_gen, obj_val)[source]

Apply tournament selection and keep the best points.

This method is used if there are multiple objectives and the non-dominated set is being kept.

Parameters
old_genndarray

Points in current generation.

obj_valndarray

Objective value of each point.

Returns
ndarray

New generation with best points.

ndarray

Corresponding objective values.

Bases: openmdao.core.driver.Driver

Driver for a simple genetic algorithm.

Parameters
**kwargsdict of keyword arguments

Keyword arguments that will be mapped into the Driver options.

Attributes
_problem_commMPI.Comm or None

The MPI communicator for the Problem.

_concurrent_pop_sizeint

Number of points to run concurrently when model is a parallel one.

_concurrent_colorint

Color of current rank when running a parallel model.

_desvar_idxdict

Keeps track of the indices for each desvar, since GeneticAlgorithm sees an array of design variables.

_ga<GeneticAlgorithm>

Main genetic algorithm lies here.

_randomstatenp.random.RandomState, int

Random state (or seed-number) which controls the seed and random draws.

__init__(**kwargs)[source]

Add a recorder to the driver.

Parameters
recorderCaseRecorder

A recorder instance.

cleanup()

Clean up resources prior to exit.

declare_coloring(num_full_jacs=3, tol=1e-25, orders=None, perturb_size=1e-09, min_improve_pct=5.0, show_summary=True, show_sparsity=False)

Set options for total deriv coloring.

Parameters
num_full_jacsint

Number of times to repeat partial jacobian computation when computing sparsity.

tolfloat

Tolerance used to determine if an array entry is nonzero during sparsity determination.

ordersint

Number of orders above and below the tolerance to check during the tolerance sweep.

perturb_sizefloat

Size of input/output perturbation during generation of sparsity.

min_improve_pctfloat

If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used.

show_summarybool

If True, display summary information after generating coloring.

show_sparsitybool

If True, display sparsity with coloring info after generating coloring.

get_constraint_values(ctype='all', lintype='all', driver_scaling=True)

Return constraint values.

Parameters
ctypestr

Default is ‘all’. Optionally return just the inequality constraints with ‘ineq’ or the equality constraints with ‘eq’.

lintypestr

Default is ‘all’. Optionally return just the linear constraints with ‘linear’ or the nonlinear constraints with ‘nonlinear’.

driver_scalingbool

When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.

Returns
dict

Dictionary containing values of each constraint.

get_design_var_values(get_remote=True, driver_scaling=True)

Return the design variable values.

Parameters
get_remotebool or None

If True, retrieve the value even if it is on a remote process. Note that if the variable is remote on ANY process, this function must be called on EVERY process in the Problem’s MPI communicator. If False, only retrieve the value if it is on the current process, or only the part of the value that’s on the current process for a distributed variable.

driver_scalingbool

When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.

Returns
dict

Dictionary containing values of each design variable.

get_objective_values(driver_scaling=True)

Return objective values.

Parameters
driver_scalingbool

When True, return values that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model. Default is True.

Returns
dict

Dictionary containing values of each objective.

get_reports_dir()

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Returns
str

The path to the directory where reports should be written.

property msginfo

Return info to prepend to messages.

Returns
str

Info to prepend to messages.

objective_callback(x, icase)[source]

Evaluate problem objective at the requested point.

In case of multi-objective optimization, a simple weighted sum method is used:

$f = (\sum_{k=1}^{N_f} w_k \cdot f_k)^a$

where $$N_f$$ is the number of objectives and $$a>0$$ is an exponential weight. Choosing $$a=1$$ is equivalent to the conventional weighted sum method.

The weights given in the options are normalized, so:

$\sum_{k=1}^{N_f} w_k = 1$

If one of the objectives $$f_k$$ is not a scalar, its elements will have the same weights, and it will be normed with length of the vector.

Takes into account constraints with a penalty function.

All constraints are converted to the form of $$g_i(x) \leq 0$$ for inequality constraints and $$h_i(x) = 0$$ for equality constraints. The constraint vector for inequality constraints is the following:

\begin{align}\begin{aligned}g = [g_1, g_2 \dots g_N], g_i \in R^{N_{g_i}}\\h = [h_1, h_2 \dots h_N], h_i \in R^{N_{h_i}}\end{aligned}\end{align}

The number of all constraints:

$N_g = \sum_{i=1}^N N_{g_i}, N_h = \sum_{i=1}^N N_{h_i}$

The fitness function is constructed with the penalty parameter $$p$$ and the exponent $$\kappa$$:

$\Phi(x) = f(x) + p \cdot \sum_{k=1}^{N^g}(\delta_k \cdot g_k)^{\kappa} + p \cdot \sum_{k=1}^{N^h}|h_k|^{\kappa}$

where $$\delta_k = 0$$ if $$g_k$$ is satisfied, 1 otherwise

Note

The values of $$\kappa$$ and $$p$$ can be defined as driver options.

Parameters
xndarray

Value of design variables.

icaseint

Case number, used for identification when run in parallel.

Returns
float

Objective value.

bool

Success flag, True if successful.

int

Case number, used for identification when run in parallel.

record_iteration()

Record an iteration of the current Driver.

run()[source]

Execute the genetic algorithm.

Returns
bool

Failure flag; True if failed to converge, False is successful.

scaling_report(outfile='driver_scaling_report.html', title=None, show_browser=True, jac=True)

Generate a self-contained html file containing a detailed connection viewer.

Optionally pops up a web browser to view the file.

Parameters
outfilestr, optional

The name of the output html file. Defaults to ‘driver_scaling_report.html’.

titlestr, optional

Sets the title of the web page.

show_browserbool, optional

If True, pop up a browser to view the generated html file. Defaults to True.

jacbool

If True, show jacobian information.

Returns
dict

Data used to create html file.

set_design_var(name, value, set_remote=True)

Set the value of a design variable.

‘name’ can be a promoted output name or an alias.

Parameters
namestr

Global pathname of the design variable.

valuefloat or ndarray

Value for the design variable.

set_remotebool

If True, set the global value of the variable (value must be of the global size). If False, set the local value of the variable (value must be of the local size).

use_fixed_coloring(coloring=<object object>)

Tell the driver to use a precomputed coloring.

Parameters
coloringstr

A coloring filename. If no arg is passed, filename will be determined automatically.