Review of <b>How to Solve It: Modern Heuristics</b>
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
How to Solve It is a friendly gem of a book which introduces the basic principles of traditional optimization, evolutionary optimization, neural nets, and fuzzy methods. In the spirit of Polya's classic of the same name, the authors emphasize the "how and why" of the problem-solving process, constantly prodding the reader to stop and solve subproblems, or come up with new heuristics, or indeed question whether or not the problem has been posed correctly in the first place.This book is clear, concise, and fun to read. It is not a handbook of heuristics, but rather an accessible high-level overview of the pros, cons, and applicability of the major categories of heuristic methods, with particular emphasis on optimization methods utilizing evolutionary computation. The presentation is lucid, and the authors do a good job of picking out key properties of algorithms and problem domains. The only prerequisites are basic mathematics and some problem-solving talent.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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