Heritage-dynamic cultural algorithm for multi-population solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-Population Cultural Algorithms (MPCA) define a set of individuals, each belonging to one of a set of populations that determines the shared goal, behavior or knowledge space of the individual. The design of MPCA extends from Cultural Algorithms (CA), which in turn improves on Genetic Algorithms (GA). To date, all examples of MPCA restrict individuals to belong to a single population at any given time, which can limit search potential and ability to simulate scenarios inspired by observations in real life. This article adopts ancestral “Heritage” as a new paradigm to extend MPCA. We introduce the “Heritage-Dynamic Cultural Algorithm” (HDCA) to allow easier definition of heterogeneous individuals and encourage greater search potential. As a test case, HDCA is compared directly with versions of MPCA, CA and GA against single-objective numerical optimization functions to demonstrate these intentions and inspire its use for new applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it