Dakota Reference Manual: soga

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soga

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

## Topics

This keyword is related to the topics:

- package_jega
- global_optimization_methods

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | fitness_type | Select the fitness type for JEGA methods
| Optional | replacement_type | Select a replacement type for JEGA methods
| Optional | convergence_type | Select the convergence type for JEGA methods
| Optional | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods
| Optional | max_function_evaluations | Number of function evaluations allowed for optimizers
| Optional | scaling | Turn on scaling for variables, responses, and constraints
| Optional | population_size | Set the initial population size in JEGA methods
| Optional | log_file | Specify the name of a log file
| Optional | print_each_pop | Print every population to a population file
| Optional | initialization_type | Specify how to initialize the population
| Optional | crossover_type | Select a crossover type for JEGA methods
| Optional | mutation_type | Select a mutation type for JEGA methods
| Optional | seed | Seed of the random number generator
| Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence
| Optional | model_pointer | 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:

1. fitness
2. replacement
3. convergence
4. initialization
5. crossover
6. mutation
7. 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 Parameters
- Best Objective Functions (when objective_functions) are specified)
- Best Nonlinear Constraints
- Calibration (when calibration_terms are specified)

## Theory

The basic steps of the `soga` algorithm are as follows:

1. Initialize the population
2. Evaluate the population (calculate the values of the objective function and constraints for each population member)
3. Loop until converged, or stopping criteria reached
   1. Perform crossover
   2. Perform mutation
   3. Evaluate the new population
   4. Assess the fitness of each member in the population
   5. Replace the population with members selected to continue in the next generation
   6. Test for convergence

## See Also

These keywords may also be of interest:

- moga