61 research outputs found
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
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.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed
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 was reached, where 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.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization
This paper investigates the control of an ML component within the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box
optimization. The known CMA-ES weakness is its sample complexity, the number of
evaluations of the objective function needed to approximate the global optimum.
This weakness is commonly addressed through surrogate optimization, learning an
estimate of the objective function a.k.a. surrogate model, and replacing most
evaluations of the true objective function with the (inexpensive) evaluation of
the surrogate model. This paper presents a principled control of the learning
schedule (when to relearn the surrogate model), based on the Kullback-Leibler
divergence of the current search distribution and the training distribution of
the former surrogate model. The experimental validation of the proposed
approach shows significant performance gains on a comprehensive set of
ill-conditioned benchmark problems, compared to the best state of the art
including the quasi-Newton high-precision BFGS method
CMA-ES with Restarts for Solving CEC 2013 Benchmark Problems
This paper investigates the performance of 6 versions of Covariance Matrix Adaptation Evolution Strategy (CMAES) with restarts on a set of 28 noiseless optimization problems (including 23 multi-modal ones) designed for the special session on real-parameter optimization of CEC 2013. The experimental validation of the restart strategies shows that: i). the versions of CMA-ES with weighted active covariance matrix update outperform the original versions of CMA-ES, especially on illconditioned problems; ii). the original restart strategies with increasing population size (IPOP) are usually outperformed by the bi-population restart strategies where the initial mutation stepsize is also varied; iii). the recently proposed alternative restart strategies for CMA-ES demonstrate a competitive performance and are ranked first w.r.t. the proportion of function-target pairs solved after the full run on all 10-, 30- and 50-dimensional problems
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