Titlebook: Applications of Evolutionary Computation; 27th European Confer Stephen Smith,Jo?o Correia,Christian Cintrano Conference proceedings 2024 Th

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書目名稱Applications of Evolutionary Computation影響因子(影響力)




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Jon-Philip Imbrenda,Michael W. Smith-goal robot manipulation tasks with sparse rewards. Hindsight Experience Replay (HER) is an existing method that improves learning efficiency by using failed trajectories and replacing the original goals with hindsight goals that are uniformly sampled from the visited states. However, HER has a limi

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Jon-Philip Imbrenda,Michael W. Smith weights and structure of artificial neural networks. With evolutionary algorithms often failing to produce the same level of diversity as biological evolution, explicitly . with additional optimization objectives has emerged as a successful approach. However, there is a lack of knowledge regarding

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Adapting Pedagogy for Formative Assessmentogical and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning. In this work, we propose a computational model for studying a mechanism that can enable such a process. We adopt a computational framework based on meta

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Encyclopedia of Engineering Geologyarning (ERL). While recent years have witnessed the emergence of a swath of metaphor-laden approaches, many merely echo old algorithms through novel metaphors. Simultaneously, numerous promising ideas from evolutionary biology and related areas, ripe for exploitation within evolutionary machine lear

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https://doi.org/10.1007/978-1-4020-6359-6ited by simplified operator sets and pipeline structures, fail to address the full complexity of this task. Two novel metrics are proposed for measuring structural, and hyperparameter, dissimilarity in the decision space. A hierarchical approach is employed to integrate these metrics, prioritizing s

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https://doi.org/10.1007/978-1-4020-6359-6ification, a black-box adversarial attack that introduces changes to the pixels of the input images to make the classifier predict erroneously. We use a pragmatic approach by employing different evolutionary algorithms - Differential Evolution, Genetic Algorithms, and Covariance Matrix Adaptation Ev