Genetic Algorithm and Non-Linear Constraint Solver in Series and Parallel

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Hi,
I am conducting an optimisation with bound, mixed integer and non-linear constraints with the genetic algorithm solver. My objective and constraint function are costly to evaluate and share computation. I have successfully placed the objective and constraint constraints in the same function as per https://www.mathworks.com/help/optim/ug/objective-and-nonlinear-constraints-in-the-same-function.html.
However I would like to understand how effective this approach is in each of the following cases. I have included an example answers of the type of thing I hope to learn for the serial cases (not necessarily correct!). However, I cannot do the same for the parallel case as I don't understand how the computation is split up.
  • Serial computation + Augmented Langrangian Algorithm. Example answer: constraints and fitness are evaluated serially for a given individual therefore storing the last individual effectively reduces the runtime.
  • Serial computation + Penalty/Integer Algorithm. Example answer: all constraints are evaluated and then fitness for individuals that satisfy constraints are evaluated therefore storing the last individual is not an effective way of reducing the runtime.
  • Parallel computation + Augmented Langrangian Algorithm
  • Parallel computation + Penalty/Integer Algorithm
I'd hope to learn how the problem is split up in parallel and whether there a way to effectively share information between the cosntraint and fitness function in all of the above cases.
Best,
Ryan

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