Fitness Landscapes of Evolved Apoptotic Cellular Automata
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the fitness landscape for evolutionary algorithms evolving cellular automata (CA) rules to satisfy an apoptotic fitness function. This fitness function requires the automata to grow as rapidly as possible and to die out by a fixed time step. The apoptotic CA yielded rules that are extremely robust to variation, while utilizing the majority of available positions in the updating rule. Robustness is assessed by a novel technique called fertility. In addition, fitness morphs are adapted for use on discrete fitness landscapes to demonstrate the localization of high fitness rules to small portions of the fitness landscape. The fitness landscape is shown to be rugose and to be populated by many optima. Single-parent techniques are used both to improve evolutionary techniques for locating automata rules, and to generalize rules that are evolved for one case of the fitness function to other cases of that fitness function. In addition to introducing the evolution of apoptotic CA as a test problem and evolved art technique, many of the analysis tools presented are unique and applicable beyond their focus in the current study.
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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 | 0.000 | 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