Solving the MAX-SAT problem by binary enhanced fireworks algorithm
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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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".