Innervator
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Stanford University,California Institute of Technology
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 1 December 1989
- Authors
- Geoffrey Miller, Peter Todd, and Shailesh Hegde
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Mathematics
- Task
- Pattern classification
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 0K
- Training data
- 10,240 tokens
Each net has 5 units
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.2 × 10⁸ FLOP
- How it was established
- Operation counting
10 params * 6 FLOP/param/pass * 4 datapoints * 1000 epochs * 50 individuals * 10 generations
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 1,132
Sources
Where this record came from and when it was last checked.
- Reference
- Designing neural networks using genetic algorithms
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Innervator was published by Stanford University,California Institute of Technology, in the country recorded as United States of America, during December 1989. The category the publisher falls under is academia,Academia.
It works in the domain of Mathematics, and is recorded as performing the task of pattern classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required arithmetic totalling around 1.2 × 10⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 10,240 tokens of text.
Answers
Innervator — common questions
Innervator— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Innervator— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Innervator— how many parameters does it have?
It has a parameter count of 0K. Each net has 5 units. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Innervator— who created it?
It was published by Stanford University,California Institute of Technology, based in United States of America, an organisation categorised as academia,Academia.
Innervator— when was it released?
It was published in December 1989. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Innervator— what is it used for?
It works in the domain of Mathematics, and is recorded as handling the task of pattern classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Innervator— how much compute was used to train it?
Training consumed around 1.2 × 10⁸ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.