Titlebook: Genetic Programming; 21st European Confer Mauro Castelli,Lukas Sekanina,Pablo García-Sánchez Conference proceedings 2018 Springer Internati

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Multi-objective Evolution of Ultra-Fast General-Purpose Hash Functionse application-specific as well as general-purpose hash functions, where the main design target was the quality of hashing. As hash functions are frequently called in various time-critical applications, it is important to optimize their implementation with respect to the execution time. In this paper

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Evolving Better RNAfold Structure Predictionich give more accurate predictions of how RNA molecules fold up. Genetic improvement updates 29% of the dynamic programming free energy model parameters. In most cases (50.3%) GI gives better results on 4655 known secondary structures from RNA_STRAND (29.0% are worse and 20.7% are unchanged). Indeed

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Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programmingiptive/discriminative representations from raw data, we propose a structurally layered representation that allows GP to learn a feature space from large scale and high dimensional data sets. Previous efforts from the GP community for feature learning have focused on small data sets with a few input

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https://doi.org/10.1007/978-3-319-77553-1artificial intelligence; evolutionary algorithms; evolutionary computation; evolvable hardware; games; ge

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