Titlebook: Genetic Programming Theory and Practice XVIII; Wolfgang Banzhaf,Leonardo Trujillo,Bill Worzel Book 2022 The Editor(s) (if applicable) and

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仇恨

,Grammar-Based Vectorial Genetic Programming for?Symbolic Regression,t also allow vector variables. Also, the model’s abilities are extended to allow operations on vectors, where most vector operations are simply performed component-wise. Additionally, new aggregation functions are introduced that reduce vectors into scalars, allowing the model to extract information

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Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming,an be generated, improving the performance of Genetic Programming. Strongly-Typed and Grammar-Guided Genetic Programming are two examples of using domain-knowledge to improve performance of Genetic Programming by preventing solutions that are known to be invalid from ever being added to the populati

MIR

What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?,sured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly pre

Herd-Immunity

,An Exploration of?Exploration: Measuring the?Ability of?Lexicase Selection to?Find Obscure Pathwaystion. Understanding how selection schemes affect exploitation and exploration within a search space is crucial to tackling increasingly challenging problems. Here, we introduce an “exploration diagnostic” that diagnoses?a selection scheme’s capacity for search space exploration. We use our explorati

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,Back to the Future—Revisiting OrdinalGP and Trustable Models After a Decade,st small data sets to reward model generalization. ESSENCE (2009)?[.] extended the OrdinalGP concept to handle imbalanced data by using the SMITS algorithm to rank data records according to their information content to avoid locking into the behavior of heavily sampled data regions but had the disad

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