Bibliographic record
Abstract
Many-objective optimization problems (MaOPs) are multi-objective problems that have four or more objectives. MaOPs face significant challenges because of search inefficiency, computational cost, decision making, and visualization. Most MaOP systems use variants of non-dominated sorting (Pareto ranking). However, Pareto dominance is ineffective when the number of objectives exceeds four. In this research, we explore different strategies for solving MaOPs. We use Hornby's Age-Layered Population Structure (ALPS) evolutionary algorithm in order to mitigate premature convergence and improve results. Instead of Pareto ranking, we use the many-objective evaluation strategy called sum of ranks (SR). SR is more appropriate than Pareto dominance for problems that require a majority of objectives to be optimized. We introduce and compare different objective reduction methods for ALPS, including random and correlated objective reduction. Because hypervolume and IGD performance measurements are not necessarily suitable to SR strategies, we introduce a new minimum distance measurement. Results show that different strategies are suitable for different problems, and depend strongly on the performance measure being used. Random objective reduction was the least effective strategy, while correlated reduction was more successful. The research shows that the ALPS framework with objective reduction is a promising framework for MaOPs.
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Research integrity | 0.001 | 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".