Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search
Bibliographic record
Abstract
Abstract Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming. At each generation, the idea is to capture promising properties of the parent population in a probabilistic model and to use corruption to transfer variations of these properties to the offspring. This work studies the influence of corruption and sampling steps on search. Corruption partially mutates candidate solutions that are used as input to the model, whereas the number of sampling steps defines how often we re-use the output during model sampling as input to the model. We study the generalization of the royal tree problem, the Airfoil problem, and the Pagie-1 problem, and find that both corruption strength and the number of sampling steps influence exploration and exploitation in search and affect performance: exploration increases with stronger corruption and lower number of sampling steps. The results indicate that both corruption and sampling steps are key to the success of the DAE-GP: it permits us to balance the exploration and exploitation behavior in search, resulting in an improved search quality. However, also selection is important for exploration and exploitation and should be chosen wisely.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".