Bibliographic record
Abstract
Evolutionary computation (EC) is the area of computer science and engineering that concerns itself with algorithms derived from formalizing natural evolution. This is part of a larger effort to draw inspiration from biological systems for computational purposes. Evolutionary computation methods have been used to solve optimization problems, to model systems, and to recognize patterns among other application tasks. Due to their reliance on stochasticity, they are characterized as heuristic search methods. The main features of evolutionary computation methods are their reliance on populations of searchers, the stochasticity of the search processes through mutation and recombination operations, and the application of relative strength as their selection criterion. The principle of cumulative selection allows searchers to continuously improve solutions until predefined termination criteria for the algorithms are fulfilled. The literature on evolutionary computation is comprised of a large body of proposals for algorithmic variants including hybridization schemes with other algorithms; of theoretical examinations of convergence features and other characteristics of particular variants; and of empirical studies of their performance under various testing environments, which are either constructed artificially or taken from practical applications to benchmark these variants. Furthermore, individual practical applications are published as stand-alone contributions to various fields of engineering, science, and other disciplines. Besides explicit fitness, the selection criteria for solution quality driven by external purposes like particular applications, other algorithms are studied under intrinsic selection criteria like reproductive success in an environment. Algorithms of this type come under the heading of digital or computational evolution and intend to more closely model the natural systems EC algorithms draw inspiration from. This entails studies of robustness and evolvability under various systems settings, as well as examinations of the power of algorithms to provide creative novel solutions under more-natural conditions like in an ecosystem.
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.
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 | 0.005 |
| 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 | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".