Titlebook: Genetic Programming; 23rd European Confer Ting Hu,Nuno Lourenço,Federico Divina Conference proceedings 2020 Springer Nature Switzerland AG

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Benchmarking Manifold Learning Methods on a Large Collection of Datasets,reduced number of dimensions is sufficient to maintain the information about the data. In this paper, a large scale comparison of manifold learning techniques is performed for the task of classification. We show the current standing of genetic programming (GP) for the task of classification by compa

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Detection of Frailty Using Genetic Programming, and other adverse outcomes, resulting in burden to individuals, their families, health care services and society. Early detection and screening would help to deliver preventive interventions and reduce the burden of frailty. For this purpose, several studies have been conducted to detect frailty th

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Is k Nearest Neighbours Regression Better Than GP?,ncluding geometric semantic genetic programming (GSGP). However, KNN is a memorization, and not a learning, method, i.e. it evaluates unseen data on the basis of training observations, and not by running a learned model. This paper takes a first step towards the objective of defining a learning meth

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Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shopecisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling h