Surprising Creativity: Anecdotes from Evolutionary Computation
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Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#2When using evolutionary pressures to guide software, you have to remember that your stated goal (via fitness functions) often does not truly reflect your intended goal.
For example, when I was using genetic algorithms to pick stock trades, I tried to maximize total_net_gain_loss / number_of_trades. The GA quickly figured out that 0 trades was the best answer. In hindsight, Duh! But I wanted to make trades!
"The only winning move is not to play"
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#3When using evolutionary pressures to guide software, you have to remember that your stated goal (via fitness functions) often does not truly reflect your intended goal. For example, when I was using genetic algorithms to pick stock trades, I tried to maximize total_net_gain_loss / number_of_trades. The GA quickly figured out that 0 trades was the best answer. In hindsight, Duh! But I wanted to make trades! "The only…
That is one kind of problem. The 'Genie' problem: be very specific what you wish for because the genie will misinterpret you.
But it's not the only kind of problem. There are inherent biases in evolutionary systems. One example: in systems with varying length genotypes there is a massive pressure to bloat. Even if you encode 'small' as a strong requirement in your fitness function, it may not be enough (or it may be enough to completely defeat whatever your real goal was).
There are inherent biases. In the (simulated) genetics, in the genotype to phenotype mapping, in the evolutionary operators, even if you get the fitness function right.
Evolutionary computing as an engineering tool is hard.
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#4The article's philosophical discussion of "surprise" feels a little klunky and academic to me, and I think conflates some different kinds of surprise, but it's still really fun to think about.
Many of the projects here are all inspired by Karl Sims' work in the early 90's.
Sims evolved virtual creates by specifying goals to achieve and then running a physics simulation. He noted at the time that the evolution process was great at exploiting bugs in the simulation.
https://www.youtube.com/watch?v=JBgG_VSP7f8
I was insipred enough by Sims' genetic images (http://www.karlsims.com/genetic-images.html) that I spent a few years trying to get surprising and beautiful results of my own, with some limited success (https://flic.kr/s/3Xoz).
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#5Finding where your simulation model breaks down is one of two great features of evolutionary optimization. The other is its ability to preserve a population of approximate solutions rather than finding only a single optimized solution.
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#6When using evolutionary pressures to guide software, you have to remember that your stated goal (via fitness functions) often does not truly reflect your intended goal. For example, when I was using genetic algorithms to pick stock trades, I tried to maximize total_net_gain_loss / number_of_trades. The GA quickly figured out that 0 trades was the best answer. In hindsight, Duh! But I wanted to make trades! "The only…
This parallels much of mathematics. Many formulations, even in physics, admit so called trivial solutions which amount conceptually to zero.
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#7When using evolutionary pressures to guide software, you have to remember that your stated goal (via fitness functions) often does not truly reflect your intended goal. For example, when I was using genetic algorithms to pick stock trades, I tried to maximize total_net_gain_loss / number_of_trades. The GA quickly figured out that 0 trades was the best answer. In hindsight, Duh! But I wanted to make trades! "The only…
That is one kind of problem. The 'Genie' problem: be very specific what you wish for because the genie will misinterpret you. But it's not the only kind of problem. There are inherent biases in evolutionary systems. One example: in systems with varying length genotypes there is a massive pressure to bloat. Even if you encode 'small' as a strong requirement in your fitness function, it may not be enough (or it may be…
Just picking a nit, but the problem with a genie isn't that it misinterprets you but that it literally does exactly what you ask for. The mismatch between "what you wanted" and "what you asked for" isn't the genie's fault but yours.
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#8When using evolutionary pressures to guide software, you have to remember that your stated goal (via fitness functions) often does not truly reflect your intended goal. For example, when I was using genetic algorithms to pick stock trades, I tried to maximize total_net_gain_loss / number_of_trades. The GA quickly figured out that 0 trades was the best answer. In hindsight, Duh! But I wanted to make trades! "The only…
My first thought: Why not just use max gain? of course that would leas to catastrophic failure over short or long. Second thought: Why not both? Just like speed is a derivative, so perhaps you want a set of differential equations, also gain per square time, too.
On another note, "not to play" should be a zero-division error.
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#9Earlier quoted context omitted.
That is one kind of problem. The 'Genie' problem: be very specific what you wish for because the genie will misinterpret you. But it's not the only kind of problem. There are inherent biases in evolutionary systems. One example: in systems with varying length genotypes there is a massive pressure to bloat. Even if you encode 'small' as a strong requirement in your fitness function, it may not be enough (or it may be…
Just picking a nit, but the problem with a genie isn't that it misinterprets you but that it literally does exactly what you ask for. The mismatch between "what you wanted" and "what you asked for" isn't the genie's fault but yours.
Yes, the genie gives you exactly what you ask for but misinterpretes what you wish for.
Re: Surprising Creativity: Anecdotes from Evolutionary Computation
#10Off topic - I find it humorous that arXiv list 49 authors and then ends the listed author list with:
"et al. (1 additional author not shown)"
I know a line has got to be drawn somewhere but in this case, listing the last author (Jason Yosinksi) would have taken less space than the explanation that not all the authors are shown.