Plenary lecture 8: towards opposition and center-based sampling for high-dimensional search spaces
Bibliographic record
Abstract
Footprints of the opposition concept can be observed in many areas around us. But it has sometimes been called by different names. Opposite particles in physics, complement of an event in probability, absolute or relative complement in set theory, and theses and antitheses in dialectic just are some examples to mention. Recently for the first time, Opposition-Based Learning (OBL) was proposed and then the opposition-based methods have been introduced in different artificial intelligence areas. All of them have tried to enhance searching or leaning process by utilizing the opposition concept. Opposition-based evolutionary algorithms, opposition-based neural networks, and also opposition-based reinforcement learning are some efforts in this direction. The main idea behind OBL is the simultaneous consideration of a candidate and its corresponding opposite candidate in order to achieve a better approximation for the current solution. The first and second parts of this lecture introduce the opposition-based sampling and its applications in various soft computing techniques and center-based sampling, respectively. Population-based algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Evolutionary Strategies (ES) are commonly used approaches to solve complex problems from science and engineering. They work with a population of candidate solutions. In this lecture, a novel center-based sampling is introduced for these algorithms. Reducing the number of function evaluations to tackle with high-dimensional problems is a worthwhile attempt; the proposed center-based sampling can open a new research area in this direction. Our simulation results confirm that this kind of sampling, which can be utilized during population initialization and/or generating successive generations, can be valuable in solving high-dimensional problems efficiently. Quasi-Oppositional Differential Evolution (QODE) will briefly be discussed as an evidence to support the proposed sampling theory. Finally, the opposition-based sampling and center-based sampling will be compared in this lecture.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".