Quote from Option_Attack:
De-Evolution: BoWo
Yeah, jack's good at killing braincells.
Meanwhile, in the real world, here's my take on genetic optimizations and how I use them.
Multicharts has a perfectly functioning genetic optimizer, and here's I use it.
We've found that inside of 5 variables that are the key core to our system, we have to find a range of those from 1 to 1000 or 1*10^15=1000000000000000.
We break that down into boxes of 100 steps.
That is, 1-1001 has a step at 1,101,201...901,1001. Genetically optimizing those ranges we are able to narrow down our ranges further in smaller and smaller, more precise steps.
Our optimizations tonight gave us a 1-151, 51-151,1-101,1-101, 1-101 as a starting point. So we started stepping by 50's, then 10's, then 5's then 1's, whereas in the first one genetically optimizing 1,51,151 and all of the other possible values.
The end result is that we went from 1*10^15 possibilities to 120,960 possibilities optimizing genetically. Once you deduce the range of values given by succesive short lived genetic optimization, you can then perform your exhaustive studies.
Our range values optimizing genetically goes from 1000000000000000 to 120960 which is thousands of orders of magnitude easier for a computer to analyze. What I've realized learning from this process, is that if you understand genetic algorithms as computerized evolution, you can find the perfect set of parameters in quadrillions of possibilities.
Where my system stops its evolution has more to do with its automated capacity to self-optimize. I believe the OP is looking more for genetic optimizations built into the strategy automatically than the manual process I used tonight to find 120k possibilities from 1*10^15.
Multicharts is beautiful for doing this, and the software packages necessary to build a true genetic robot are simply too cumbersome and next to impossible to ever get to a point that you would deploy them on a server. Getting to that stage is a much bigger problem than a solution, since Multicharts off-the-shelf genetic optimizer can do this for you.
Assuming you have a program capable of genetic evolution, the process of narrowing down possibilities to a computable amount that won't take 5,000 years is the whole reason genetic optimization was created, and I'm saying that there is no reason to build your own packages when the off-the-shelf technology, particularly Multicharts, can do this without the additional headache of compiling data, and coding an actual software package to both analyze and implement. Both analysis and implementation is way too much to ask the typical newbie genetic programmer how to do.
The pitfalls of using this is that many do not have strategies suitable for these methods. If I were to ask you what the probability of there being another planet like earth with similarly sentient species, the probabilities are about as large before genetic algorithms were created as the time it would take to compute 1000^5 possibilities on a PC. Next to supercomputers, that many possiblities is impossible to compute, but if you're smart you'll use genetic optimization in boxes if you have more than 3 variables to optimize and are able to narrow your ranges to a reasonably short period of time.
