Intelligent design vs random mutations

Intelligent design vs random mutations

A transcript of the above talk

Hi, I'm Tim Tyler, and today I'll be addressing the issue of the relative merits of intelligent design and genetic algorithms.

Intelligent design and genetic algorithms

Both intelligent design and genetic algorithms represent optimisation strategies. Optimisation is a type of search which is guided by a utility function.

Genetic algorithms are search strategy based on random mutations, recombination and selection.

Intelligent design is a search strategy based on the actions of an intelligent agent in solving the problem.

Framed in this way, it might seem obvious that an intelligent agent would have a substantial advantage in any contest - since they can always elect to use a genetic algorithm if they so choose - but could also use any other search algorithm - if they felt that the problem demanded it.

However, the situation is not a no-brainer - there are computational overheads to intelligence - maybe the genetic algorithm will have solved the problem before the intelligent agent has decided what approach they will use.

The promise of genetic algorithms

In order to illustrate the promise of genetic algorithms here's a clip from Richard Dawkins in 1987 - explaining the virtues of the approach:

[Clip of Richard Dawkins from Horizon: The Blind Watchmaker]

Essentially, Dawkins makes two points: one is that in many complex problems it's hard to do much better than trial and error anyway - and the other is that genetic algorithms allow for a rapid, automated search.

The failure of genetic algorithms

The dream of evolutionary optimisation which Dawkins spelled out has pretty miserably failed to materialise. Evolutionary optimisation techniques are used by engineers in solving problems - but they have a pretty poor reputation: a generic search technique which you try in those rare cases where you have little other information about the structure of a problem - besides a utility function.

There are several problems with the approach.

One is that it is often hard to express what you actually want as a utility function in the first place. You might know what robustness or maintainability are - but expressing such things to a computer is not always trivial. This is a problem with all automated search techniques, of course. Specification languages are there to help with this problem, but they are not there yet.

If you don't ask for exactly what you want, you often get something which is brittle, incomprehensible, or otherwise unsuitable. For example, if you are writing a computer program, one criterion is often that the code should be self-documenting. If you don't know how to tell the computer about this what you will get back will often be incomprehensible, unmaintainable spaghetti code.

Also, automated search techniques often only seem to works on small problems - and those are problems which humans can often solve easily by other means.

What about the points that Dawkins made? Yes, automating a search sometimes helps - though genetic algorithms are not the only automated search technique. However, it does not seem to be true that in many complex problems it's hard to do much better than trial and error. Making changes at random is a particularly stupid approach - and usually it is easy to beat it.

The future

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.


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