Dakota Reference Manual  Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Single-objective Genetic Algorithm (a.k.a Evolutionary Algorithm)


This keyword is related to the topics:


Alias: none

Argument(s): none

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 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 linear_inequality_constraint_matrix Define coefficients of the linear inequality constraints
Optional linear_inequality_lower_bounds Define lower bounds for the linear inequality constraint
Optional linear_inequality_upper_bounds Define upper bounds for the linear inequality constraint
Optional linear_inequality_scale_types Specify how each linear inequality constraint is scaled
Optional linear_inequality_scales Define the characteristic values to scale linear inequalities
Optional linear_equality_constraint_matrix Define coefficients of the linear equalities
Optional linear_equality_targets Define target values for the linear equality constraints
Optional linear_equality_scale_types Specify how each linear equality constraint is scaled
Optional linear_equality_scales Define the characteristic values to scale linear equalities
Optional model_pointer

Identifier for model block to be used by a method


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.


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.

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.


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: