Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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package_jega

Description

The JEGA library[18] contains two global optimization methods. The first is a Multi-objective Genetic Algorithm (MOGA) which performs Pareto optimization. The second is a Single-objective Genetic Algorithm (SOGA) which performs optimization on a single objective function. Both methods support general constraints and a mixture of real and discrete variables. The JEGA library was written by John Eddy, currently a member of the technical staff in the System Readiness and Sustainment Technologies department at Sandia National Laboratories in Albuquerque. These algorithms are accessed as moga and soga within Dakota.

Related Topics

Related Keywords

  • moga : Multi-objective Genetic Algorithm (a.k.a Evolutionary Algorithm)
  • soga : Single-objective Genetic Algorithm (a.k.a Evolutionary Algorithm)