Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on evolutionary computat ion have been presented. According to news originating from the Tokyo Institute of Technology by NewsRx correspondents, research stated, “The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving continuous black-box optimization problems.” Our news journalists obtained a quote from the research from Tokyo Institute of Technology: “A practically useful aspect of CMA-ES is that it can be used withou t hyperparameter tuning. However, the hyperparameter settings still have a consi derable impact on performance, especially for difficult tasks, such as solving multimodal or noisy problems. This study comprehensively explores the impact of learning rate on CMA-ES performance and demonstrates the necessity of a small learning rate by considering ordinary differential equatio ns. Thereafter, it discusses the setting of an ideal learning rate. Based on the se discussions, we develop a novel learning rate adaptation mechanism for CMA-ES that maintains a constant signal-to-noise ratio.”
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