Evolutionary systems & genetic algorithms
From GenerativeArt
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For engineering solutions with structured (non-bitstring) genes the distribution of mutations may be non-uniform, e.g. Gaussian (or Normal). Some studies have shown such techniques provide faster more accurate solutions. | For engineering solutions with structured (non-bitstring) genes the distribution of mutations may be non-uniform, e.g. Gaussian (or Normal). Some studies have shown such techniques provide faster more accurate solutions. | ||
| - | {{SingleImage|imageWidthPlusTen= | + | {{SingleImage|imageWidthPlusTen=640|imageURL=http://www-viz.tamu.edu/courses/viza658/wiki/genetic/03.jpg|caption=Comparision of mutation distributions}} |
In addition the rate of mutation may be a genetic variable. This provides some relief in deciding what the mutation rate should be. The optimal mutation rate will then evolve. It's even possible that the mutation rate will self-adjust over time. Typically at the start the mutation rate should be higher (randomization) than it is later (when fine tuning). It's worth noting that some cases have been found in nature where the mutation rate increases in response to increased environmental pressure. This too could be simulated by calculating the mutation rate as a function of both genetics and environmental pressure. | In addition the rate of mutation may be a genetic variable. This provides some relief in deciding what the mutation rate should be. The optimal mutation rate will then evolve. It's even possible that the mutation rate will self-adjust over time. Typically at the start the mutation rate should be higher (randomization) than it is later (when fine tuning). It's worth noting that some cases have been found in nature where the mutation rate increases in response to increased environmental pressure. This too could be simulated by calculating the mutation rate as a function of both genetics and environmental pressure. | ||
(In non-artistic optimization applications genetic algorithms often ramp down the frequency and size of mutations as the system converges on a solution. This is often referred to as "simulated annealing.") | (In non-artistic optimization applications genetic algorithms often ramp down the frequency and size of mutations as the system converges on a solution. This is often referred to as "simulated annealing.") | ||
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== An Aside Regarding Bit String Genetic Representations == | == An Aside Regarding Bit String Genetic Representations == | ||
