Summary
Evolutionary Algorithms provide a general approach to inverse problem solving: As optimization methods, they only require the computation of values of the function to optimize. Thus, the only prerequisite to efficiently handle inverse problems is a good numerical model of the direct problem, and a representation for potential solutions.
The identification of mechanical inclusion, even in the linear elasticity framework, is a difficult problem, theoretically ill-posed: Evolutionary Algorithms are in that context a good tentative choice for a robust numerical method, as standard deterministic algorithms have proven inaccurate and unstable. However, great attention must be given to the implementation. The representation, which determines the search space, is critical for a successful application of Evolutionary Algorithms to any problem. Two original representations are presented for the inclusion identification problem, together with the associated evolution operators (crossover and mutation). Both provide outstanding results on simple instances of the identifica tion problem, including experimental robustness in presence of noise.
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Schoenauer, M., Jouve, F., Kallel, L. (1997). Identification of Mechanical Inclusions. In: Dasgupta, D., Michalewicz, Z. (eds) Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03423-1_26
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DOI: https://doi.org/10.1007/978-3-662-03423-1_26
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