Evolution as a search algorithm is known (from what I remember from studying
CS theory a while back) to be optimal in some senses: given enough time and
some diversity considerations in can find global maxima in very complex search spaces.
Evolution by random mutations pretty-much sucks as a search strategy:
“One of the reasons genetic algorithms get used at all is because we do not yet have machine intelligence. Once we have access to superintelligent machines, search techniques will use intelligence ubiquitously. Modifications will be made intelligently, tests will be performed intelligently, and the results will be used intelligently to design the next generation of trials.
There will be a few domains where the computational cost of using intelligence outweighs the costs of performing additional trials—but this will only happen in a tiny fraction of cases.
Even without machine intelligence, random mutations are rarely an effective strategy in practice. In the future, I expect that their utility will plummet—and intelligent design will become ubiquitous as a search technique.”
I listened to your talk until I realized I could just read the essay :)
I partly agree with you. You say:
Evolution by random mutations pretty-much sucks as a search strategy:
Sucks is not quite descriptive enough. Random mutation is slow, but that is not really relevant to my point—as I said—given enough time it is very robust. And sex transfer speeds that up dramatically, and then intelligence speeds up evolutionary search dramatically.
yes intelligent search is a large—huge—potential speedup on top of genetic evolution alone.
But we need to understand this in the wider context … you yourself say:
One of the reasons genetic algorithms get used at all is because we do not yet have machine intelligence.
Ahh but we already have human intelligence.
Intelligence still uses an evolutionary search strategy, it is just internalized and approximate. Your brain considers a large number of potential routes in a highly compressed statistical approximation of reality, and the most promising eventually get written up or coded up and become real designs in the real world.
But this entire process is still all evolutionary.
And regardless, the approximate simulation that intelligence such as our brain uses does have limitations—mainly precision. Some things are just way too complex to simulate accurately in our brain, so we have to try them in detailed computer simulations.
Likewise, if you are designing a simple circuit space, then a simpler GA search running on a fast computer can almost certainly find the optimal solution way faster than a general intelligence—similar to an optimized chess algorithm.
A general intelligence is a huge speed up for evolution, but it is just one piece in a larger system .. You also need deep computer simulation, and you still have evolution operating at the world-level
Intelligence still uses an evolutionary search strategy, it is just internalized and
approximate. Your brain considers a large number of potential routes in a highly
compressed statistical approximation of reality, and the most promising eventually
get written up or coded up and become real designs in the real world. But this entire
process is still all evolutionary.
In the sense that it consists of copying with variation and differential reproductive success, yes.
However, evolution using intelligence isn’t the same as evolution by random mutations—and you originally went on to draw conclusions about the optimality of organic evolution—which was mostly the “random mutations” kind.
Evolution by random mutations pretty-much sucks as a search strategy:
“One of the reasons genetic algorithms get used at all is because we do not yet have machine intelligence. Once we have access to superintelligent machines, search techniques will use intelligence ubiquitously. Modifications will be made intelligently, tests will be performed intelligently, and the results will be used intelligently to design the next generation of trials.
There will be a few domains where the computational cost of using intelligence outweighs the costs of performing additional trials—but this will only happen in a tiny fraction of cases.
Even without machine intelligence, random mutations are rarely an effective strategy in practice. In the future, I expect that their utility will plummet—and intelligent design will become ubiquitous as a search technique.”
http://alife.co.uk/essays/intelligent_design_vs_random_mutations/
I listened to your talk until I realized I could just read the essay :)
I partly agree with you. You say:
Sucks is not quite descriptive enough. Random mutation is slow, but that is not really relevant to my point—as I said—given enough time it is very robust. And sex transfer speeds that up dramatically, and then intelligence speeds up evolutionary search dramatically.
yes intelligent search is a large—huge—potential speedup on top of genetic evolution alone.
But we need to understand this in the wider context … you yourself say:
Ahh but we already have human intelligence.
Intelligence still uses an evolutionary search strategy, it is just internalized and approximate. Your brain considers a large number of potential routes in a highly compressed statistical approximation of reality, and the most promising eventually get written up or coded up and become real designs in the real world.
But this entire process is still all evolutionary.
And regardless, the approximate simulation that intelligence such as our brain uses does have limitations—mainly precision. Some things are just way too complex to simulate accurately in our brain, so we have to try them in detailed computer simulations.
Likewise, if you are designing a simple circuit space, then a simpler GA search running on a fast computer can almost certainly find the optimal solution way faster than a general intelligence—similar to an optimized chess algorithm.
A general intelligence is a huge speed up for evolution, but it is just one piece in a larger system .. You also need deep computer simulation, and you still have evolution operating at the world-level
In the sense that it consists of copying with variation and differential reproductive success, yes.
However, evolution using intelligence isn’t the same as evolution by random mutations—and you originally went on to draw conclusions about the optimality of organic evolution—which was mostly the “random mutations” kind.