Regulatory genotype-to-phenotype mappings improve evolvability in genetic programming
Why this work is in the frame
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Bibliographic record
Abstract
Most genotype-to-phenotype mappings in EAs are redundant, i.e., multiple genotypes can map to the same phenotype. Phenotypes are accessible from one to another through point mutations. However, these mutational connections can be unevenly distributed among phenotypes. Quantitative analysis of such connections helps better characterize the robustness and evolvability of an EA. In this study, we propose two genotype-to-phenotype mapping mechanisms for linear genetic programming (LGP), where the execution and output of a linear genetic program are varied by a regulator. We investigate how such regulatory mappings can alter the genotypic connections among different phenotypes and the robustness and evolvability of phenotypes. We also compare the search ability of LGP using the conventional mapping versus the regulatory mappings, and observe that the regulatory mappings improve the efficiency in all three search scenarios, including random walk, hill climbing, and novelty search.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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