Augmented Lagrangian Constraint Handling for CMA-ES---Case of a Single Linear Constraint
Résumé
We consider the problem of minimizing a function f subject to a single inequality constraint g(x) <= 0, in a black-box scenario. We present a co-variance matrix adaptation evolution strategy using an adaptive augmented La-grangian method to handle the constraint. We show that our algorithm is an instance of a general framework that allows to build an adaptive constraint handling algorithm from a general randomized adaptive algorithm for unconstrained optimization. We assess the performance of our algorithm on a set of linearly constrained functions, including convex quadratic and ill-conditioned functions, and observe linear convergence to the optimum.
Domaines
Informatique [cs]Origine | Fichiers produits par l'(les) auteur(s) |
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