A Genetic Algorithm for calculating minimum distance between convex and concave bodies
Bibliographic record
Abstract
Distance determination, i.e. obtaining the distance between a pair of objects, is used in di¤erent applications such as the simulation of physical systems and robot path planning. Most of the existing algorithms focus on obtaining the separation distance and are limited to deal only with convex objects. In this work, a novel method for solving the minimum separation distance between convex and/or concave objects is presented. The method is based on the global optimization technique known as Genetic Algorithms (GA). Unlike previously developed works based on the use of optimization techniques to obtain the minimum distance amongst objects, the one presented here is not limited to convex objects, i.e. it does not require the concave objects to be partitioned into convex pieces. A simple local optimization method is also presented. It is shown that this method accelerates the convergence of the global stochastic search algorithm. A few examples with simple and complex objects are presented. The results obtained using di¤erent variations of the minimum distance method are compared. Particular attention is focused on the computational expense to obtain the solution as well as the precision of the solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".