Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed

2012/04/24 by Ilya Loshchilov, Marc Schoenauer, Loshchilov, Ilya +4 · 1 citation
Computer Science · Engineering · #Advanced Algorithms and Applications #Blind Source Separation Techniques #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #cs.NE

paper · pdf · doi:10.48550/arxiv.1206.0974

Genetic and Evolutionary Computation Conference (GECCO 2012) (2012)

arxiv created 2012/04/24 · openalex publication_date 2012/04/24 · arxiv updated 2012/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we study the performance of IPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy. The algorithm was tested using restarts till a total number of function evaluations of 10⁶D was reached, where D is the dimension of the function search space. The experiments show that the surrogate model control allows IPOP-saACM-ES to be as robust as the original IPOP-aCMA-ES and outperforms the latter by a factor from 2 to 3 on 6 benchmark problems with moderate noise. On 15 out of 30 benchmark problems in dimension 20, IPOP-saACM-ES exceeds the records observed during BBOB-2009 and BBOB-2010.

Cited by

• Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed

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