Tackling Deceptive Optimization Problems Using Opposition-based DE with Center-based Latin Hypercube Initialization
Bibliographic record
Abstract
Deceptiveness is among hard to tackle characteristics of the optimization problems. So far, a few papers are published in this area, while there are many real-world deceptive optimization problems. In a deceptive problem, the landscape does not provide useful information in order to progress toward the global optimum. In another word, it tends towards the deceptive attractors. As a result, finding the global optimum is a challenging task in this family of optimization problems. The goal of this paper is to propose a population-based algorithm for solving these problems. opposition-based learning (OBL) is a well-established approach to enhance meta-heuristic algorithms. Based on OBL concept, the opposite of a candidate solution is generated. Then, based on the objective values of the candidate solution and its opposite, the OBL selects the best. In another word, the proposed algorithm generates opposite of good and bad candidates in the population to break out the deceptiveness of objective function. opposition-based DE is using OBL during population initialization and also during its iterations. The ODE version proposed in this paper is different from the original ODE algorithm; in fact the scheme to generate the opposite candidate is redefined differently. Another approach used in this paper is Latin Hypercube Sampling (LHS). LHS is a statistical method to generate random samples using a multidimensional distribution. This paper combines a modified LHS and OBL with differential evolution algorithm to tackle deceptiveness in the landscape. In order to evaluate the efficiency of the proposed algorithm, some shifted benchmark functions with various characteristics are utilized. The results verify the performance superiority of the proposed algorithm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".