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

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Fips: Objectives and Achievementsn Boolean networks, abstract models of GRNs suitable for refining into synthetic biology implementations, and show how they can be used to control cell states within a range of executable models of biological systems.

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Multi-level Grammar Genetic Programming for Scheduling in Heterogeneous Networkswith a small restricted grammar and introducing the full functionality after 10 generations outperforms the state-of-the-art, even when varying the algorithm used to generate the initial population and the maximum initial tree depth.

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Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell Statesn Boolean networks, abstract models of GRNs suitable for refining into synthetic biology implementations, and show how they can be used to control cell states within a range of executable models of biological systems.

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A Comparative Study on Crossover in Cartesian Genetic Programmingenges. Our results show that it is possible for a crossover operator to outperform the standard . strategy on a limited number of tasks. The question of finding a universal crossover operator in CGP remains open.

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0302-9743 rks, generation of redundant features, ensembles of GP models, automatic design of grammatical representations, GP and neuroevolution, visual reinforcement learning, evolution of deep neural networks, evolution of graphs, and scheduling in heterogeneous networks..978-3-319-77552-4978-3-319-77553-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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P. J. Lewi,G. Calomme,J. Van Hoofprogramming approach. Initial experiments show that our proposed method can automatically create difficult, redundant features which have the potential to be used for creating high-quality feature selection benchmark datasets.