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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raction and Absorption of Modules). GLEAM’s flexible architecture and tunable parameters allow researchers to test different methods related to the generation, propagation, and use of modules in genetic programming.

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Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming,iduals in the context of Christiansen Grammatical Evolution and Refined-Typed Genetic Programming. We present three new approaches for the population initialization procedure of semantically constrained GP that are more efficient and promote more diversity than traditional Grammatical Evolution.

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What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?,y computation, and (2) these metrics better predict the long-term success of a run of evolutionary computation. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics. Moreover, our results suggest that phylogenetic diversity is indeed a better predictor of success.

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Physikalische Begriffsbildungenssover, reduces storage in an N multi-threaded implementation for a population M to .0.63M+N, compared to the usual M+2N. Memory efficient crossover achieves 692 billion GP operations per second, 692 giga GPops, at runtime on a 16 core 3.8 GHz desktop.

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Feature Discovery with Deep Learning Algebra Networks,inant analysis to train the deep learning algebra network. These enhanced algebra networks are trained on ten theoretical classification problems with good performance advances which show a clear statistical performance improvement as network architecture is expanded.

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