DNA is intelligently coded?

[logical muse]

Evolutionary computation is a very interesting field. I'm working on a project right now that will (we hope) automate searches for software vulnerabilities using genetic algorithms. It's useful stuff. muse is giving a very cogent explaination, so I won't steal his thunder.

Delphi, thanks for that, but please, explain away. It appears that you are still working in evolutionary computing and I'm not, so I'm sure there others here as well as I who would like to hear what you have to say.

 

[delphi_ote]

So why haven't oysters "decided" to sprout legs, crawl out of the murky water, and develop a more complex brain so that they too can go on vacations?

Why hasn't the moon decided to dance the Macarena?

 

[hammegk]

If it's a given in life, then you should not be bothered by its being included in a simulation of life.

Point taken, but I'm still thinking about this. Thanks. Dymanic's comment, "Reproduction should not be an explicit goal for an individual program, but a consequence of successfully solving other problems -- just as biological organisms (say, humans) often seek goals which, though they do not explicitly include reproduction, tend to lead to that outcome." adds another layer of complexity.

We're looking for evidence that macro-evolution is impossible, right?

I'm looking for evidence. I do not state macro-ev is impossible, only as yet I don't find evidence that convinces me.

By the same token, if you look around you and take it on faith that a goal was involved, it's also your choice.

I'm agnostic, and take neither position, seeing imo evidence for both views.


delphi_ote: I see what you are driving at with 'evolutionary computation' (or genetic algorithms) rather than 'computation'. Although wikipedia mentions a bunch of problems suited for the technique, the basis most people accept is that evolutionary biology was the source of concepts used: inheritance, mutation, natural selection, and recombination (or crossover). Also the abstract representations of candidate solutions (called individuals) are called 'chromosomes'.

 

[delphi_ote]

delphi_ote: I see what you are driving at with 'evolutionary computation' (or genetic algorithms) rather than 'computation'. Although wikipedia mentions a bunch of problems suited for the technique, the basis most people accept is that evolutionary biology was the source of concepts used: inheritance, mutation, natural selection, and recombination (or crossover). Also the abstract representations of candidate solutions (called individuals) are called 'chromosomes'.

Indeed. Evolutionary computation (the field to which which genetic algorithms, genetic programming, evolutionary strategies, and other related studies belong, and which is also generally viewed as a subset of machine learning) was inspired by the evolutionary process. In fact, there has recently been a very interesting tradeoff between evolutionary biology and evolutionary computation. Evolutionary computation researchers are finding quite frequently that mimicking nature's paradigm is the most successful way to produce robust and novel solutions, and evolutionary biology is trying to simulate evolution more and more accurately. The two fields are bleeding together in some ways. The really crazy part is that we're even starting to make computers out of DNA (look up "DNA computing" if you're interested.) One day, we may very well have DNA computers using evolutionary algorithms to solve biology problems!

Kudos for reading up on stuff, hammy.