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Augmented Lagrangian Constraint Handling for CMA-ES---Case of a Single Linear Constraint
Abstract : 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.
Cited literature [11 references]
https://hal.inria.fr/hal-01390386
Contributor : Asma Atamna <>
Submitted on : Tuesday, November 1, 2016 - 9:13:17 PM
Last modification on : Wednesday, September 16, 2020 - 5:11:40 PM
Long-term archiving on: : Tuesday, March 14, 2017 - 5:13:55 PM
Contributor : Asma Atamna <>
Submitted on : Tuesday, November 1, 2016 - 9:13:17 PM
Last modification on : Wednesday, September 16, 2020 - 5:11:40 PM
Long-term archiving on: : Tuesday, March 14, 2017 - 5:13:55 PM
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PPSN-2016.pdf
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