Dakota Reference Manual
Version 6.2
LargeScale Engineering Optimization and Uncertainty Analysis

Singleobjective 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 Singleobjective 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:
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:
These keywords may also be of interest: