REVIEW OF MODERN USE OF GENETIC AND EVOLUTIONARY ALGORITHMS. STRATEGIES, POSSIBILITIES (REVIEW ARTICLE)
Bibliographic record
Abstract
Modern trends in the optimal and rational design of technical objects cross a large number of directions of their implementation. One of the interesting and promising directions is genetic and evolutionary algorithms (GА and EA). Authors promote the use of GА and EA as a tool for solving problems of optimal and rational design of complex mechanical systems. The relevance of highlighting modern methods, approaches and strategies for the implementation of GА and EA is described, as well as consideration of their applied implementation, which makes it possible to identify interesting directions of research that, with further adaptation or modifications, can be used to solve the problems of optimal and rational design of gearboxes, boxes gears and transmissions. The main general directions of the literature on GА and EA are highlighted, as well as the practical use of GА and EA in: technical and technological activities, physics, construction, water systems, nanotechnologies, analytical and simulation modeling, electrical and electronic systems, modeling of artificial intelligence and neural networks, information technologies, economic theory, administration and management, marketing, sociology, biology and medicine. This made it possible to understand the course of scientific thought on this issue, to determine the advantages and disadvantages of existing directions and approaches, and helped to choose the vector of further scientific thought, to decide on interesting approaches, strategies and methods. Considering certain features of EA, the authors prefer them. And in terms of strategies, hybridization with other methods, maximum saturation of all stages with "randomness" and the possibility of learning (memory organization) of the algorithm similar to neural networks are promising. Keywords: optimal design, research directions, genetic algorithms, evolutionary algorithms
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".