Back in the 1990s, some of us were working on using genetic algorithms (simulated evolutionary methods) to evolve neural network architectures. This was during one of the AI winters, between the late 1980s flurry of neural network research based on back-propagation, and the early 2000s rise of deep learning in much larger networks.
Some examples of this work are here (designing neural networks with genetic algorithms, 1989), here (genetic algorithms for autonomous robot control systems, 1994), here (artificial evolution as a path towards AI, 1997), and here (technological evolution through genetic algorithms, 2000). I’m citing mostly work I did at Stanford with Peter Todd and with the cognitive science group at U. Sussex (UK), such as Dave Cliff.
There was a lot of other similar work at the time in the research areas of genetic algorithms, artificial life, autonomous agents, and evolutionary robotics, published in conference proceedings with those kinds of titles and key words.
Most of this work didn’t directly address AI safety issues, however, such as safe selection pressures for digital minds. But it might be of interest for some historical context around these issues. And the work did try to make some connections between neural networks, simulated evolution, autonomous agents, cognitive evolution, and evolutionary psychology.
Thanks, I did a MSc in this area back in the early 2000s, my system was similar to Tierra, so I’m familiar with evolutionary computation history. Definitely useful context. Learning classifier systems are also interesting to check out for aligning multi-agent evolutionary systems. It definitely informs where I am coming from.
Do you know anyone with this kind of background that might be interested in writing something long form on this? I’m happy to collaborate, but my mental health has not been the best. I might be able to fund this a small bit, if the right person needs it.
Back in the 1990s, some of us were working on using genetic algorithms (simulated evolutionary methods) to evolve neural network architectures. This was during one of the AI winters, between the late 1980s flurry of neural network research based on back-propagation, and the early 2000s rise of deep learning in much larger networks.
Some examples of this work are here (designing neural networks with genetic algorithms, 1989), here (genetic algorithms for autonomous robot control systems, 1994), here (artificial evolution as a path towards AI, 1997), and here (technological evolution through genetic algorithms, 2000). I’m citing mostly work I did at Stanford with Peter Todd and with the cognitive science group at U. Sussex (UK), such as Dave Cliff.
There was a lot of other similar work at the time in the research areas of genetic algorithms, artificial life, autonomous agents, and evolutionary robotics, published in conference proceedings with those kinds of titles and key words.
Most of this work didn’t directly address AI safety issues, however, such as safe selection pressures for digital minds. But it might be of interest for some historical context around these issues. And the work did try to make some connections between neural networks, simulated evolution, autonomous agents, cognitive evolution, and evolutionary psychology.
Thanks, I did a MSc in this area back in the early 2000s, my system was similar to Tierra, so I’m familiar with evolutionary computation history. Definitely useful context. Learning classifier systems are also interesting to check out for aligning multi-agent evolutionary systems. It definitely informs where I am coming from.
Do you know anyone with this kind of background that might be interested in writing something long form on this? I’m happy to collaborate, but my mental health has not been the best. I might be able to fund this a small bit, if the right person needs it.