Dakota Reference Manual
Version 6.12
Explore and Predict with Confidence

Multiobjective Genetic Algorithm (a.k.a Evolutionary Algorithm)
This keyword is related to the topics:
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  niching_type  Specify the type of niching pressure  
Optional  convergence_type  Select the convergence type for JEGA methods  
Optional  postprocessor_type  Post process the final solution from moga  
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 
moga
stands for Multiobjective Genetic Algorithm, which is a global optimization method that does Pareto optimization for multiple objectives. It supports general constraints and a mixture of real and discrete variables.
Constraints
moga
can utilize linear constraints using the keywords: * linear_inequality_constraint_matrix * linear_inequality_lower_bounds * linear_inequality_upper_bounds * linear_inequality_scale_types * linear_inequality_scales * linear_equality_constraint_matrix * linear_equality_targets * linear_equality_scale_types * linear_equality_scales
Configuration
The genetic algorithm configurations are:
The steps followed by the algorithm are listed below. The configurations will effect how the algorithm completes each step.
Stopping Criteria
The moga
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.
The specification for convergence in a moga can either be metric_tracker
or can be omitted all together. If omitted, no convergence algorithm will be used and the algorithm will rely on stopping criteria only.
Expected Outputs
The moga
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.
Note that moga and SOGA create additional output files during execution. "finaldata.dat" is a file that holds the final set of Pareto optimal solutions after any postprocessing is complete. "discards.dat" holds solutions that were discarded from the population during the course of evolution.
It can often be useful to plot objective function values from these files to visually see the Pareto front and ensure that finaldata.dat solutions dominate discards.dat solutions. The solutions are written to these output files in the format "Input1...InputN..Output1...OutputM".
Expected HDF5 Output
If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5:
Important Notes
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.
If using the fitness types layer_rank
or domination_count
, it is strongly recommended that you use the replacement_type
below_limit
(although the roulette wheel selectors can also be used).
The functionality of the domination_count selector of JEGA v1.0 can now be achieved using the domination_count
fitness type and below_limit
replacement type.
The basic steps of the moga
algorithm are as follows:
If moga is used in a hybrid optimization method (which requires one optimal solution from each individual optimization method to be passed to the subsequent optimization method as its starting point), the solution in the Pareto set closest to the "utopia" point is given as the best solution. This solution is also reported in the Dakota output.
This "best" solution in the Pareto set has minimum distance from the utopia point. The utopia point is defined as the point of extreme (best) values for each objective function. For example, if the Pareto front is bounded by (1,100) and (90,2), then (1,2) is the utopia point. There will be a point in the Pareto set that has minimum L2norm distance to this point, for example (10,10) may be such a point.
If moga is used in a method which may require passing multiple solutions to the next level (such as the surrogate_based_global
method or hybrid
methods), the orthogonal_distance
postprocessor type may be used to specify the distances between each solution value to winnow down the solutions in the full Pareto front to a subset which will be passed to the next iteration.
These keywords may also be of interest: