Abstract
This work proposes and presents a preliminary investigation of a fitness evaluation scheme supported by a proper genotype representation intended to guide an under development expansion to EASEA/EASEA-CLOUD platforms to evolve partial differential equations as models for a specific system of interest, starting with measures from that system. A simple proof of concept using a dynamic bidirectional surface wave is presented, showing that the proposed fitness evaluation scheme is very promising to enable automate system modelling, even when dealing with up to \(\pm 10\,\%\) noise-added data.
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Notes
- 1.
The number of sub-domains has as an upper bound the number of available points in the dataset.
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Acknowledgements
I. S. Peretta would like to thank the non-simultaneous support received from CAPES (PDSE scholarship #18386-12-1) and CNPq (Full PhD scholarship - GD), both Brazilian funding agencies.
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Peretta, I.S., Yamanaka, K., Bourgine, P., Collet, P. (2015). Proposal and Preliminary Investigation of a Fitness Function for Partial Differential Models. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_15
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DOI: https://doi.org/10.1007/978-3-319-16501-1_15
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