Hello,
Here's a nice use case for the BlackBox solver in PROC OPTMODEL (SAS Optimization) :
Maximize product quality with Optimization and Machine Learning models
by NATALIA SUMMERVILLE on MARCH 23, 2021
https://blogs.sas.com/content/subconsciousmusings/2021/03/23/maximize-product-quality-with-optimizat...
In SAS 9.4 I used PROC OPTLSO for this task
, now I call BlackBox solver in PROC OPTMODEL. It's in both cases Local Search Optimization based on Genetic Algorithms.
Why?
• Incorporating non-closed-form and nonlinear models (such as Neural Nets or Gradient Boosting) in optimization does not allow for traditional sound-and-proof algorithms to work (such as branch-and-bound or simplex).
• You need cutting-edge (nonlinear optimization) solvers.
• SAS has the black-box solver for this task !
The Black-Box Optimization Solver
- The black-box solver is based on a genetic algorithm (GA).
- GAs are a family of local search algorithms that seek optimal solutions to problems by applying the principles of natural selection and evolution.
- You can apply genetic algorithms to almost any optimization problem. They are especially useful for problems where calculus-based techniques do not work, such as when the objective function has many local optima, when the objective function is not differentiable or continuous, or when solution elements are constrained to be integers or sequences.
- In most cases, genetic algorithms require more computation than specialized techniques that take advantage of specific problem structures or characteristics. However, for optimization problems for which no such techniques are available, genetic algorithms provide a robust general method of solution.
BR,
Koen
