Age-Layered Strategies for Many-Objective Optimization
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
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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