Maintaining Diversity in The Bounded Pareto-Set
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For more than two decades, stand-alone evolutionary multi-objective optimization (EMO) methods have been adequately demonstrated to find a set of trade-off solutions near Pareto-front for various multi-objective optimization problems. Despite a long-standing existence of classical generative single-objective based methods, a very few EMO studies have combined the two approaches for a better gain. In this paper, we investigate the effect of seeding the initial population of an EMO algorithm with extreme solutions obtained using a single-objective method. Our proposed approach is further aided with an opposition based offspring creation mechanism which strategically places new solutions on the current Pareto frontier by a simple, yet a novel arbitration policy that utilizes the relative distances from the extreme solutions in the current population members. We conduct an extensive simulation of our proposed approach on a wide variety of two and three-objective benchmark MOP test problems. Results are shown to be remarkably better than the original EMO approach in terms of hyper-volume metric. The results are interesting and should motivate EMO researchers to integrate single-objective focused optimization and an opposition-based concept with diversity-preserving EMO procedures for an overall better performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it