Enhancing discrete differential evolution by conducting election
Bibliographic record
Abstract
Differential Evolution (DE) is a population-based algorithm which has been successfully used to solve optimization problems. DE algorithm begins with an initial population with some randomly generated candidate solutions. During evolutionary process, the population of candidate solutions is evolved toward the promising region by using the specific operations. The population in the DE algorithm can resemble an especial perspective of a small society which has individuals to seek a common goal. In a society, the election system is commonly used as an effective approach; which is employed to determine one or several representatives who are responsible to make major decisions. Some machine learning algorithms are inspired from the society election system to develop an enhanced algorithm from a pool of potential algorithms with the complementary performances. This study is motivated from the election systems of societies which can be applied on population-based algorithms, here DE algorithm as a case study. We propose an election-based discrete DE algorithm which uses the information of all candidate solutions to create a new trial solution as a president candidate solution. During optimization phases, after applying the evolutionary operators, all candidate solutions vote to select the values of president's variables. In the proposed method, a majority voting method is applied to choose a value for each variable of the president candidate solution. We employ the discrete DE (DDE) algorithm as the parent algorithm to develop election-based discrete DE (EDDE) which is evaluated on the fifteen discrete benchmark functions. Simulation results confirm that EDDE obtains a promising performance on the majority of these functions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".