soga

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• Keywords Area
• method
• soga

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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

	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

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.

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.

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