Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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The GA-P: A Genetic Algorithm and Genetic Programming Hybrid
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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.