The effect of elitist fitness-based selection on the escape from local optima
Bibliographic record
Abstract
The concept of escape from local optima is formally defined in continuous domains to mean that a previously found locally optimal solution is replaced by a search solution (i.e., it has survived selection) that is in a different attraction basin. Exploration that does not lead to an update is not considered to have achieved an escape from a local optimum. The search solutions (blue circles) are unable to update the previously found local optimum (red star) and thus allow the metaheuristic to escape from the local optimum. Random Search is the baseline that a metaheuristic must improve upon to be worth its added complexity. Random Search, in the form of Hill Climbing, cannot escape from local optima. A key claim of many metaheuristics is that they are able to escape from local optima. However, these claims are poorly tested and often based on imprecise definitions of what it means to escape from a local optimum in continuous domain search spaces. A practical and precise definition for an escape from a local optimum is developed. It is then shown how elitist fitness-based selection can lead to the rejection of exploratory search solutions, and this can cause many popular metaheuristics to degrade into (localized) Random Search in their attempts to escape from local optima. The explosion of new metaheuristics has often been just a repeated re-invention of localized Random Search for the key task of escaping from local optima. • A formal definition for escaping from local optima is developed for continuous domain search spaces. • The effect of elitist fitness-based selection is shown to hinder the ability of many metaheuristics to escape from local optima in continuous domains. • Key differences between combinatorial and continuous domains are presented.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".