What's a genetic algorithm that would produce interesting/surprising results and not have a boring/obvious end point?

I find genetic algorithm simulations like this to be incredibly entrancing and I think it'd be fun to make my own. But the problem with most simulations like this is that they're usually just hill climbing to a predictable ideal result that could have been crafted with开发者_Python百科 human guidance pretty easily. An interesting simulation would have countless different solutions that would be significantly different from each other and surprising to the human observing them.

So how would I go about trying to create something like that? Is it even reasonable to expect to achieve what I'm describing? Are there any "standard" simulations (in the sense that the game of life is sort of standardized) that I could draw inspiration from?

Depends on what you mean by interesting. That's a pretty subjective term. I once programmed a graph analyzer for fun. The program would first let you plot any f(x) of your choice and set the bounds. The second step was creating a tree holding the most common binary operators (+-*/) in a random generated function of x. The program would create a pool of such random functions, test how well they fit to the original curve in question, then crossbreed and mutate some of the functions in the pool.

The results were quite cool. A totally weird function would often be a pretty good approximation to the query function. Perhaps not the most useful program, but fun nonetheless.

Well, for starters that genetic algorithm is not doing hill-climbing, otherwise it would get stuck at the first local maxima/minima.

Also, how can you say it doesn't produce surprising results? Look at this vehicle here for example produced around generation 7 for one of the runs I tried. It's a very old model of a bicycle. How can you say that's not a surprising result when it took humans millennia to come up with the same model?

To get interesting emergent behavior (that is unpredictable yet useful) it is probably necessary to give the genetic algorithm an interesting task to learn and not just a simple optimisation problem.

For instance, the Car Builder that you referred to (although quite nice in itself) is just using a fixed road as the fitness function. This makes it easy for the genetic algorithm to find an optimal solution, however if the road would change slightly, that optimal solution may not work anymore because the fitness of a solution may have grown dependent on trivially small details in the landscape and not be robust to changes to it. In real, cars did not evolve on one fixed test road either but on many different roads and terrains. Using an ever changing road as the (dynamic) fitness function, generated by random factors but within certain realistic boundaries for slopes etc. would be a more realistic and useful fitness function.

I think EvoLisa is a GA that produces interesting results. In one sense, the output is predictable, as you are trying to match a known image. On the other hand, the details of the output are pretty cool.