Exploring Long-term Memory in Evolutionary Multi-objective Algorithms: A Case Study with NSGA-III
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
In the field of many-objective optimization, obtaining a dense solution set is a challenging task, mostly due to having hyper-surface nature of Pareto-front; which cannot be covered by commonly utilized population sizes. This is particularly vital in scenarios where innovization and informed decision-making are crucial. The challenge stems from the constraints imposed by population size limitations in evolutionary algorithms, which impede the efficient exploration of multiple solutions. A contributing factor to this issue is the lack of long-term memory in the well-known evolutionary algorithms to retain these solutions. On the contrary, the effective training of machine learning-assisted optimization or innovization relies on a substantial amount of data, which can be provided by preserving these valuable solutions. Moreover, long-term memory can play a significant role in expensive many-objective optimization, where the repetition of the optimization process is both costly and time-consuming, similar to training deep neural networks. The study focuses on NSGA-III equipped with long-term memory and assessing its performance across 16 benchmark problems, encompassing DTLZ1 to DTLZ7 and WFG1 to WFG9, considering scenarios with 3, 5, and 10 objectives. This paper explores the benefits of incorporating long-term memory in terms of the ultimate optimization outcomes, including the number of non-dominated solutions, knee points, and Inverted Generational Distance (IGD).
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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 | 0.004 |
| 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