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

anthropologist

阻止

Hybrid Surrogate Assisted Evolutionary Multiobjective Reinforcement Learning for Continuous Robot Coding these optimal policies (known as Pareto optimal policies) for different preferences of objectives requires extensive state space exploration. Thus, obtaining a dense set of Pareto optimal policies is challenging and often reduces the sample efficiency. In this paper, we propose a hybrid multiob

浸软

松驰

Leveraging More of Biology in Evolutionary Reinforcement Learningarning (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

秘方药

A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Searchited 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

deviate

Insatiable

多产子

Robust Neural Architecture Search Using Differential Evolution for Medical Imagestions. Adversarial attacks on medical images may cause manipulated decisions and decrease the performance of the diagnosis system. The robustness of medical systems is crucial, as it assures an improved healthcare system and assists medical professionals in making decisions. Various studies have bee

公社

ANTE

Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Dcies, varying imaging conditions, and limited data. Existing flower localization methods face limitations, including reliance on color information, low model interpretability, and a large demand for training data. This paper proposes a new genetic programming (GP) approach called ACFGP with a novel