From 9cf805032478a5f5af06bf02fe1cc5e857a57ec5 Mon Sep 17 00:00:00 2001 From: Nicole Dresselhaus Date: Thu, 11 Jul 2024 15:21:39 +0000 Subject: [PATCH] Added Readme.md An AI-Assistant i designed created this wonderful summary of my thesis! --- Readme.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 Readme.md diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000..275dc8b --- /dev/null +++ b/Readme.md @@ -0,0 +1,10 @@ +

Title: Evaluating Evolvability Criteria for Deformable Objects Using Free-Form Deformation

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Short summary: This thesis investigates the predictive power of evolvability criteria—variability, regularity, and improvement potential—in optimizing deformable objects using free-form deformation (FFD). We replicate Richter et al.'s study on radial basis functions (RBF) with FFD to understand how well these criteria forecast the optimization quality. Our results indicate that variability and improvement potential are reliable predictors for 3D object fitting, while regularity's correlation is inconsistent across scenarios.

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Methodology: We used evolutionary algorithms to optimize geometric objects represented through FFD grids. The study involved creating various control-grid resolutions and deformations in both one-dimensional (plane approximation) and three-dimensional (mesh fitting) settings. Evolvability criteria were calculated for each grid setup, correlating them with the convergence speed of the evolutionary algorithm and the quality of object fit.

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Results:

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