Back in my undergrad days, a fellow student of mine implemented a genetic algorithm on a field-programmable gate array with the intention of performing computations. Once he got the thing working in the first place, it took him half a semester to get it able to pass the 7 bits from the 7 input channels to the 7 output channels, in order. He didn’t have time left over to try anything more complicated.
Well genetic algorithms work by making assumptions about the problem space, mainly that better solutions are very likely to be found close to other good solutions. If the assumption is not true or only weakly true, than of course it isn’t going to work. Like if beneficial mutations are extremely rare or practically non-existent.
My point is that it depends entirely on the problem and how it’s represented. Some problems work really well for GAs, and some don’t at all.
Back in my undergrad days, a fellow student of mine implemented a genetic algorithm on a field-programmable gate array with the intention of performing computations. Once he got the thing working in the first place, it took him half a semester to get it able to pass the 7 bits from the 7 input channels to the 7 output channels, in order. He didn’t have time left over to try anything more complicated.
So, yeah.
Well genetic algorithms work by making assumptions about the problem space, mainly that better solutions are very likely to be found close to other good solutions. If the assumption is not true or only weakly true, than of course it isn’t going to work. Like if beneficial mutations are extremely rare or practically non-existent.
My point is that it depends entirely on the problem and how it’s represented. Some problems work really well for GAs, and some don’t at all.