%0 Generic
%A Carrascal, Alberto
%A Alberdi, Amaia
%T Evolutionary industrial physical model generation
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%D 2010
%@ 0302-9743
%U https://hdl.handle.net/11556/2063
%X Both complexity and lack of knowledge associated to physical processes makes physical models design an arduous task. Frequently, the only available information about the physical processes are the heuristic data obtained from experiments or at best a rough idea on what are the physical principles and laws that underlie considered physical processes. Then the problem is converted to find a mathematical expression which fits data. There exist traditional approaches to tackle the inductive model search process from data, such as regression, interpolation, finite element method, etc. Nevertheless, these methods either are only able to solve a reduced number of simple model typologies, or the given black-box solution does not contribute to clarify the analyzed physical process. In this paper a hybrid evolutionary approach to search complex physical models is proposed. Tests carried out on a real-world industrial physical process (abrasive water jet machining) demonstrate the validity of this approach.
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