Skip to Main content Skip to Navigation
Conference papers

Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 1, 3
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.
Complete list of metadatas

Cited literature [29 references]  Display  Hide  Download

https://hal.inria.fr/hal-00686570
Contributor : Loshchilov Ilya <>
Submitted on : Tuesday, April 10, 2012 - 3:53:11 PM
Last modification on : Wednesday, April 8, 2020 - 3:23:10 PM
Document(s) archivé(s) le : Wednesday, July 11, 2012 - 2:54:07 AM

Files

paper511.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00686570, version 1
  • ARXIV : 1204.2356

Collections

Citation

Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy. Genetic and Evolutionary Computation Conference (GECCO 2012), Jul 2012, Philadelphia, United States. pp.321-328. ⟨hal-00686570⟩

Share

Metrics

Record views

565

Files downloads

1040