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This is a repository copy of Model predictive control of non-domestic heating using genetic programming dynamic models.

High-performance electromagnetic interference (EMI) shielding materials with ultralow density and environment-friendly properties are greatly demanded to address electromagnetic radiation pollution. Herein, carbon nanotube/polylactic acid (CNT/PLA) materials with different CNT contents, which exhibit characteristics of light weight, environmental protection and good chemical stability, are fabricated using 3D printing technology, where CNTs are evenly distributed and bind well with PLA. The performances of 3D-printed CNT/PLA composites are improved compared to pure 3D-printed PLA composites, which include mechanical properties, conductive behaviors and electromagnetic interference (EMI) shielding. The EMI shielding effectiveness (SE) of CNT/PLA composites could be improved when the content of CNTs increase. When it reaches 15 wt%, the EMI SE of 3D-printed CNT/PLA composites could get up to 47.1 dB, which shields 99.998% of electromagnetic energy. Meanwhile, the EMI shielding mechanism of 3D-printed CNT/PLA composites is mainly of absorption loss, and it generally accounts for more than 80% of the total shielding loss. These excellent comprehensive performances endow a 3D-printed CNT/PLA composite with great potential for use in industrial and aerospace areas.

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We report a series of experiments that use semantic-based local search within a multiobjective genetic programming (GP) framework. We compare various ways of selecting target subtrees for local search as well as different methods for performing that search; we have also made comparison with the random desired operator of Pawlak et al. using statistical hypothesis testing. We find that a standard steady state or generational GP followed by a carefully-designed single-objective GP implementing semantic-based local search produces models that are mode accurate and with statistically smaller (or equal) tree size than those generated by the corresponding baseline GP algorithms. The depth fair selection strategy of Ito et al. is found to perform best compared with other subtree selection methods in the model refinement.