Morphid Academy: a virtual laboratory for evolution of form and function
Bibliographic record
Abstract
Physical forms are a vital part of our daily lives and can be seen all around us. Some forms are static, others move around, but all have a certain desired function. Forms and their associated functions are normally created by human hands or using various human-made machinery and manufacturing processes. Some complex forms, such as living organisms, have been created through complex evolutionary forces. It is those forces that are of interest in improving the human-made forms. This thesis tackles the problem of evolution of physically simulated forms and their associated functions. In order to harness the power of natural evolution, the forces behind it have to be understood and controlled. Evolution is not very practical to study in nature due to its very long time scale. It can, however, be studied in computer simulations using models of form and function. We have created Morphid Academy (MA) - a virtual laboratory for the study of evolution for physical forms and their functions. This evolutionary framework allows for the generation, simulation, evolution, and visualization of various models of forms. The aspect of form and its function is encapsulated into a physically simulated agent called a Morphid. The MA studies Morphid evolution through an experimental framework composed of structured simulation entities called Training Grounds. A special Morphocosm Training Ground is an ecosystem of interacting Morphids. To demonstrate the Morphid Academy on a concrete example, we have implemented a virtual creature model of a Morphid. The virtual creature Morphid model, composed of a morphology and neural controller, allowed us to use MA in order to evolve virtual creatures in the training tasks of locomotion and light following. To showcase MA functionality, complex experimental topologies were designed to study incremental evolution, neural network evolution, and adaptation to environmental perturbations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".