Abstract
For continuous optimization problems, evolutionary algorithms (EAs) that build and use probabilistic models have obtained promising results. However, the local gradient information of the fitness function is not used in these EAs. In the case of optimization of continuous differentiable functions, it may be less efficient to disregard this information.
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