818 research outputs found
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
Alternative Restart Strategies for CMA-ES
This paper focuses on the restart strategy of CMA-ES on multi-modal
functions. A first alternative strategy proceeds by decreasing the initial
step-size of the mutation while doubling the population size at each restart. A
second strategy adaptively allocates the computational budget among the restart
settings in the BIPOP scheme. Both restart strategies are validated on the BBOB
benchmark; their generality is also demonstrated on an independent real-world
problem suite related to spacecraft trajectory optimization
Identification of the Isotherm Function in Chromatography Using CMA-ES
This paper deals with the identification of the flux for a system of
conservation laws in the specific example of analytic chromatography. The
fundamental equations of chromatographic process are highly non linear. The
state-of-the-art Evolution Strategy, CMA-ES (the Covariance Matrix Adaptation
Evolution Strategy), is used to identify the parameters of the so-called
isotherm function. The approach was validated on different configurations of
simulated data using either one, two or three components mixtures. CMA-ES is
then applied to real data cases and its results are compared to those of a
gradient-based strategy
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely
accepted as a robust derivative-free continuous optimization algorithm for
non-linear and non-convex optimization problems. CMA-ES is well known to be
almost parameterless, meaning that only one hyper-parameter, the population
size, is proposed to be tuned by the user. In this paper, we propose a
principled approach called self-CMA-ES to achieve the online adaptation of
CMA-ES hyper-parameters in order to improve its overall performance.
Experimental results show that for larger-than-default population size, the
default settings of hyper-parameters of CMA-ES are far from being optimal, and
that self-CMA-ES allows for dynamically approaching optimal settings.Comment: 13th International Conference on Parallel Problem Solving from Nature
(PPSN 2014) (2014
Use of the q-Gaussian mutation in evolutionary algorithms
Copyright @ Springer-Verlag 2010.This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.This work was supported in part by FAPESP and CNPq in Brazil and in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant EP/E060722/1 and Grant EP/E060722/2
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
Compact silicon photonics circuit to extract multiple parameters for process control monitoring
We present a compact interferometer circuit to extract multiple model parameters of on-chip waveguides and directional couplers from optical measurements. The compact design greatly improves the accuracy of extraction with fewer measurements, making it useful for process monitoring and detailed wafer-level variability analysis. We discuss the design requirements and illustrate the extraction using the Restart-CMA-ES global optimization algorithm. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen
CMA-ES with Two-Point Step-Size Adaptation
We combine a refined version of two-point step-size adaptation with the
covariance matrix adaptation evolution strategy (CMA-ES). Additionally, we
suggest polished formulae for the learning rate of the covariance matrix and
the recombination weights. In contrast to cumulative step-size adaptation or to
the 1/5-th success rule, the refined two-point adaptation (TPA) does not rely
on any internal model of optimality. In contrast to conventional
self-adaptation, the TPA will achieve a better target step-size in particular
with large populations. The disadvantage of TPA is that it relies on two
additional objective functio
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