Self-Adaptation of Meta-Parameters for Lamarckian-Inherited Neuromodulated Neurocontrollers in the Pursuit-Evasion Game
Bibliographic record
Abstract
Determining meta-parameter settings is a longstanding challenge in evolutionary computing, and often involves running repeated simulations with successive estimated values until acceptable values are found. The term metaevolution is often used to describe the optimization of one or more of the meta-parameters that control, for example, the rates of selection and variation in evolutionary optimization algorithms. Here we use self-adaptation to simultaneously evolve all of the practical meta-parameters together with the other evolved parameters as part of the main evolutionary algorithm. The meta-parameters are added to the gene, and evolved along with the other parameters. The evolved neurocontrollers with self-adaptation are compared to those with manually selected meta-parameters. Secondly, self-adapted meta-parameters from the best neurocontroller are used to evolve a further set of non-self-adapted neurocontrollers, and compared with the selfadapted and manually selected results. The effects of selfadaptation are determined through a series of experiments using a previously demonstrated multi-objective Lamarckianinherited neuromodulated evolutionary neurocontroller. The fitness of the evolved neurocontrollers is determined through their operation of a simulated vehicle pursuing a basic evader vehicle in the pursuit-evasion game. Both vehicles are subject to the effects of mass and drag. It is shown that self-adaptation can be used to automatically tune and control meta-parameters during evolution. Only a trivial amount of computational cost is added, and no degradation in evolutionary performance was observed. Under some circumstances self-adaptation may lead to improved performance of the evolutionary algorithm.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".