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On the Behaviour of the (1, λ)-ES for Conically Constrained Linear Problems

On the Behaviour of the (1, λ)-ES for Conically Constrained Linear Problems We study the behaviour of a -ES that handles constraints by resampling infeasible candidate solutions for linear optimization problems with a conically constrained feasible region. The analysis generalizes prior work in that no particular orientation of the cone relative to the gradient of the objective function is assumed. Expressions that describe the strategy's single-step behaviour are derived. Assuming that the mutation strength is adapted in a scale-invariant manner, a simple zeroth-order model is used to determine the speed of convergence of the strategy. We then derive expressions that approximately characterize the average step size and convergence rate attained when using cumulative step size adaptation and compare the values with optimal ones. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Evolutionary Computation MIT Press

On the Behaviour of the (1, λ)-ES for Conically Constrained Linear Problems

Evolutionary Computation , Volume 22 (3) – Sep 1, 2014

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Publisher
MIT Press
Copyright
© 2014 Massachusetts Institute of Technology
ISSN
1063-6560
eISSN
1530-9304
DOI
10.1162/EVCO_a_00125
pmid
24605845
Publisher site
See Article on Publisher Site

Abstract

We study the behaviour of a -ES that handles constraints by resampling infeasible candidate solutions for linear optimization problems with a conically constrained feasible region. The analysis generalizes prior work in that no particular orientation of the cone relative to the gradient of the objective function is assumed. Expressions that describe the strategy's single-step behaviour are derived. Assuming that the mutation strength is adapted in a scale-invariant manner, a simple zeroth-order model is used to determine the speed of convergence of the strategy. We then derive expressions that approximately characterize the average step size and convergence rate attained when using cumulative step size adaptation and compare the values with optimal ones.

Journal

Evolutionary ComputationMIT Press

Published: Sep 1, 2014

Keywords: Evolution strategies; constraint handling; cumulative step size adaptation

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