Solving the MAX-SAT problem by binary enhanced fireworks algorithm
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Bibliographic record
Abstract
In this paper, we present a binary enhanced firework algorithm (BEFWA) for optimization problems with a search space of binary vectors, and we test this new algorithm for the MAX-SAT problem. The EFWA algorithm is a relatively recent development in swarm intelligence (SI) for continuous optimization, and the explosion amplitude operator in EFWA does not fit for searching a good solution in a discrete binary space. The original ABC algorithm is also not suitable for searching through a binary space, but its adaptation, the discrete ABC (DisABC), for a binary space was recently presented. In the present paper, we employ the similarity-measure-based differential expression from DisABC to design the binary EFWA algorithm to operate in binary space. The MAX-SAT is a well-known modelling framework for various computationally challenging problems, and thus it has many applications. However, the MAX-SAT problem has been proven to be NP-hard. Existing results indicate that evolutionary algorithms (EAs) can be useful for finding good-quality solutions without excessive computational resources. Our experimental results demonstrate that the binary EFWA can be a better choice over DisABC and Genetic Algorithm (GA) for various classes of MAX-SAT instances.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it