Improving Tabu Search Performance by Means of Automatic Parameter Tuning
Bibliographic record
Abstract
A common problem when performing (meta)heuristic techniques over complex combinatorial optimization problems is parameter tuning. Finding the right parameter values can lead to significant improvements in terms of the best solution objective value found by the heuristic, heuristic reliability, and heuristic convergence, among others. Unfortunately, this is usually a tedious and complicated task if done manually. Furthermore, parameter values usually depend on the problem that is going to be solved. In this paper, we propose a framework that is based on the genetic programming (GP) technique to fine tune a key parameter of the well-known tabu search (TS) algorithm. Several experiments are performed over a set of small instances of the well-known capacitated facility location problem. The results have shown that adjusting the probability of acceptance of the best neighbor ρ in the TS algorithm using GP leads to an average value of the obtained solution that is closer to the optimal solution than the average value obtained by the simple TS algorithm with an a priori selected value for ρ. More importantly, standard deviation of the algorithm is greatly improved by our approach, which makes it much more reliable if time limitations are present. Finally, we confirm that the value of the parameter ρ largely depends on the problem that is attempted to solve.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".