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MMES: Mixture Model-Based Evolution Strategy for Large-Scale Optimization
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DOI：10.1109/TEVC.2020.3034769.png)
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摘要
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This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to the Gaussian sampling in CMA-ES, the proposed method generates mutation vectors from a mixture model, which facilitates exploiting the rich variable correlations of the problem landscape within a limited time budget. We analyze the probability distribution of this mixture model and show that it approximates the Gaussian distribution of CMA-ES with a controllable accuracy. We use this sampling method, coupled with a novel method for mutation strength adaptation, to formulate the mixture model-based evolution strategy (MMES)-a CMA-ES variant for large-scale optimization. The numerical simulations show that, while significantly reducing the time complexity of CMA-ES, MMES preserves the rotational invariance, is scalable to high dimensional problems, and is competitive against the state-of-the-arts in performing global optimization.
Keyword:
Covariance matrices
Frequency modulation
Gaussian distribution
Optimization
Probability distribution
Standards
Correlation
Covariance matrix adaptation
evolution strategy
large-scale optimization
mixture model
mutation strength adaptation
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12
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1.9K
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2.4W
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