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

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Related Keywords

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