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Improving Evolutionary Strategies with Generative Neural Networks
[article]
2019
arXiv
pre-print
Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions. This paper investigates the potential benefits of using highly flexible search distributions in classical ES algorithms, in contrast to standard ones (typically Gaussians). We model such distributions with Generative Neural Networks (GNNs) and introduce a new training algorithm that leverages their
arXiv:1901.11271v1
fatcat:rwcy6tuud5bijhv7nqiigf7oki