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Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming.

, , and . ICTAI, page 577-584. IEEE Computer Society, (2016)

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Planning and acting in dynamic environments: identifying and avoiding dangerous situations., , and . J. Exp. Theor. Artif. Intell., 34 (6): 925-948 (2022)Controlling the Charging of Electric Vehicles with Neural Networks.. IJCNN, page 1-8. IEEE, (2018)Multi-objectivization and Surrogate Modelling for Neural Network Hyper-parameters Tuning., and . ICIC (3), volume 375 of Communications in Computer and Information Science, page 61-66. Springer, (2013)Feature Extraction for Surrogate Models in Genetic Programming., and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 335-344. Springer, (2016)Implementing and evaluating parallel evolutionary algorithms in modern GPU computing libraries., and . GECCO Companion, page 506-509. ACM, (2022)Evolving Controllers for Electric Vehicle Charging.. EvoApplications, volume 10784 of Lecture Notes in Computer Science, page 247-255. Springer, (2018)Evolving Workflow Graphs Using Typed Genetic Programming., , and . SSCI, page 1407-1414. IEEE, (2015)Multi-objective evolution of machine learning workflows., , and . SSCI, page 1-8. IEEE, (2017)Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation., , , and . ICMLA (2), page 36-41. IEEE, (2012)978-1-4673-4651-1.Implementation of Parameter Space Search for Meta Learning in a Data-Mining Multi-agent System., , , and . ICMLA (2), page 366-369. IEEE Computer Society, (2011)978-0-7695-4607-0.