Population Stream-Driven Scalable Evolutionary 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
Solving multi-objective optimization problems with scalable decision variables and objectives is an ongoing challenging task. This study proposes a new evolutionary framework that a series of continuously generated subpopulations are used to approximate the entire Pareto-optimal front. These dynamic subpopulations are abstracted as a population stream. In this framework, one subpopulation is only responsible for searching for a Pareto-optimal solution. Diversity is emphasized among converged solutions coming from different subpopulations, striving to alleviate the conflict between diversity and convergence. To improve the convergence of the newly generated subpopulations, the polynomial fitting method is performed on the obtained solutions to model the relationships among decision variables, which are then used to assist in the generation of new subpopulations. Moreover, an adaptive granularity grid-based environmental selection strategy is proposed to maintain a set of well-diversifying converged solutions. Lastly, extensive experiments are conducted to demonstrate the proposal's superiority by comparing it with 19 representative algorithms in 45 test instances with 3-15 objectives and 300-1500 decision variables.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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