Generational Information Transfer with Neuroevolution on Control Tasks
Why this work is in the frame
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Bibliographic record
Abstract
Traditional genetic algorithms compute fitness scores every generation for all agents in a population, which typically requires agents to perform a task until they either fail or succeed. These evaluations can turn into a computational bottleneck when tasks are either time-consuming or infinite. A common workaround is to set an expiration time on agent trials, i.e. to evaluate agents on a subset of the task, which can bias the true task objective. We propose to address this bias through a novel information inheritance genetic algorithm where three distinct attributes can be passed down generations: the task environment state (agents resume from where their parents reached task expiration), the memory state (agents inherit internal representations from their parents); and the fitness (ancestors evaluation scores are compounded with an agent's own score for selection). We benchmark the various combinations of these three inherited attributes by running a genetic neuroevolution algorithm on popular feature-based control tasks. We report that information inheritance can lead to substantial increases in both data and runtime efficiency, suggesting it may greatly benefit a variety of genetic algorithm techniques and applications in the future. We provide the relevant source code.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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