Titlebook: Genetic Programming; 27th European Confer Mario Giacobini,Bing Xue,Luca Manzoni Conference proceedings 2024 The Editor(s) (if applicable) a

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https://doi.org/10.1007/978-3-031-56957-9artificial intelligence; computer programming; computer systems; correlation analysis; distributed syste

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978-3-031-56956-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Bernd Geng M.A. Soz.,Frieder Dünkel last level cache (LLC) shared between CPUs. In an 8 core 3.6?GHz desktop using multiple local searches, the Magpie parameter tuning genetic improvement (GI) system was able to reduce L3 cache access (load + stores) four fold on an existing open source 7000 line C PARSEC parallel computing VIPS image benchmark.

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Das ?konometrische Programmsystem EPSuce a new fuzzification scheme for categorical features with too many categories by using target encoding followed by the same scheme for numerical features, which is straightforward to implement, and avoids a much higher increase in the number of fuzzy features.

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https://doi.org/10.1007/978-3-642-94420-8riments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable whe

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https://doi.org/10.1007/978-3-662-30031-2ed for selection as matrix multiplication instead of recursive loops of comparisons, which in turn allows us to take advantage of optimized and parallel algorithms designed for matrix multiplication for speedup. Furthermore, we show that we can interpolate between the behavior of lexicase selection

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