# soga

Single-objective Genetic Algorithm (a.k.a Evolutionary Algorithm)

**Topics**

package_jega, global_optimization_methods

**Specification**

*Alias:*None*Arguments:*None

**Child Keywords:**

Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|

Optional |
Select the fitness type for JEGA methods |
||

Optional |
Select a replacement type for JEGA methods |
||

Optional |
Select the convergence type for JEGA methods |
||

Optional |
Number of iterations allowed for optimizers and adaptive UQ methods |
||

Optional |
Number of function evaluations allowed for optimizers |
||

Optional |
Turn on scaling for variables, responses, and constraints |
||

Optional |
Set the initial population size in JEGA methods |
||

Optional |
Specify the name of a log file |
||

Optional |
Print every population to a population file |
||

Optional |
Specify how to initialize the population |
||

Optional |
Select a crossover type for JEGA methods |
||

Optional |
Select a mutation type for JEGA methods |
||

Optional |
Seed of the random number generator |
||

Optional |
Stopping criterion based on objective function or statistics convergence |
||

Optional |
Identifier for model block to be used by a method |

**Description**

`soga`

stands for Single-objective Genetic Algorithm, which is a
global optimization method that supports general constraints and a
mixture of real and discrete variables. `soga`

is part of the JEGA
library.

*Constraints*
`soga`

can utilize linear constraints.

*Configuration*

The genetic algorithm configurations are:

fitness

replacement

convergence

initialization

crossover

mutation

population size

The pool of potential members is the current population and the current set of offspring. Choice of fitness assessors is strongly related to the type of replacement algorithm being used and can have a profound effect on the solutions selected for the next generation.

*Stopping Criteria*

The `soga`

method respects the `max_iterations`

and
`max_function_evaluations`

method independent controls to provide
integer limits for the maximum number of generations and function
evaluations, respectively.

The algorithm also stops when convergence is reached. This involves repeated assessment of the algorithm’s progress in solving the problem, until some criterion is met.

*Expected Outputs*
The `soga`

method respects the `output`

method independent control
to vary the amount of information presented to the user during
execution.

The final results are written to the Dakota tabular output. Additional
information is also available - see the `log_file`

and
`print_each_pop`

keywords.

*Expected HDF5 Output*

If Dakota was built with HDF5 support and run with the
`hdf5`

keyword, this method
writes the following results to HDF5:

Best Objective Functions (when

`objective_functions`

) are specified)Calibration (when

`calibration_terms`

are specified)

**Theory**

The basic steps of the `soga`

algorithm are as follows:

Initialize the population

Evaluate the population (calculate the values of the objective function and constraints for each population member)

Loop until converged, or stopping criteria reached

Perform crossover

Perform mutation

Evaluate the new population

Assess the fitness of each member in the population

Replace the population with members selected to continue in the next generation

Test for convergence