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Overview
The SGOPT optimization library provides an object-oriented interface to
a variety of optimization algorithms, especially stochastic optimization methods
used for global optimization. This library grew out of the C++ library
I developed during my thesis research to perform optimization with genetic
algorithms and Monte Carlo sampling. The main focus of the library
currently is genetic algorithms. SGOPT includes a generic C++ genetic algorithm
facility that is similar to the GAlib package. Additionally, it contains
mechanisms for GA-local search hybrids, as well as an implementation of
evolutionary pattern search algorithms.
Sandia is currently funding several individuals to expand the capabilities of SGOPT.
Our current plans are to expand this library to include the following methods:
- Simulated Annealing
- Tabu Search
- Clustering-base Optimization
- Bayesian Optimization
- Branch-and-Bound
Additionally, the functionality of SGOPT is being expanded to provided
general parallelism (e.g. parallelized function evaluations) as well as
the ability to perform constrained optimization.
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