A bi-objective multi-population genetic algorithm with applications to function optimization and ellipse detection
Bibliographic record
Abstract
This dissertation presents a novel Bi-objective Multi-population Genetic Algorithm (BMPGA) for multimodal optimization problems. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multi-population strategy and a clustering scheme, both of which together focus selection pressure within sub-populations, resulting in improved exploitation of promising optimum areas as well as effective identification and retention of potential optima. The practical value of BMPGA is demonstrated in several applications. In optimization of benchmark multimodal functions, it shows clear superiority over other typical multimodal GAs: Multinational GA [1], Dynamic Niche Clustering [2] and Clearing [3], with respect to overall effectiveness, general applicability and reliability. In the application of imagery ellipses detection, BMPGA is compared with both widely used Randomized Hough Transform (RHT) [4] and Sharing Genetic Algorithm (SGA) [5]. In thorough and fair experimental tests, utilizing both synthetic and real-world images, BMPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition - even in the presence of noise or/and multiple imperfect ellipses in an image - and speed of computation. Finally, we successfully extend BMPGA to the segmentation of microscopic cells, which is a necessary first step of many automated biomedical image processing procedures
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".