The paper introduces a multi-deme, memetic global optimization strategy Hierarchic memetic Strategy (HMS) especially wellsuited to the solution of a class of parametric inverse problems. This strategy develops dynamically a tree of dependent populations (demes) searching with the various accuracy growing from the root to the leaves. The search accuracy is associated with the accuracy of solving direct problems by hp-adaptive Finite Element Method. Throughout the paper we describe details of exploited accuracy adaptation and computational cost reduction mechanisms, an agent-based architecture of the proposed system, a sample implementation and preliminary benchmark results.
CITATION STYLE
Smołka, M., & Schaefer, R. (2014). A memetic framework for solving difficult inverse problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8602, pp. 138–149). Springer Verlag. https://doi.org/10.1007/978-3-662-45523-4_12
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