Evolutionary Solvers: Coupling
March 4, 2011
This is the fourth post in a series on Evolutionary Solvers.
Coupling is the process of finding mates. Once a genome has been elected to mate by the active Selection Algorithm, it has to pick a mate from the population to complete the act. There are of course many ways in which mate selection could occur, but Galapagos at the moment only allows one; selection by genomic distance. In order to explain this in detail, I should first tell you how a Genome Map works. This
is a Genome Map. It displays all the genomes (individuals) in a certain population as dots on a grid. The distance between two genomes on the grid is roughly analogous with the distance between the genomes in gene-space. I say roughly because it is in fact impossible to draw a map with exact distances. A single genome is defined by a number of genes. We assume that all the genomes in a species have the same number of genes (this is not technically a limitation of Evolutionary Algorithms, even though it is currently a limitation of Galapagos). Therefore the distance between two genomes is an N-Dimensional value, where N equals the number of genes. It is not possible to accurately display an N-Dimensional point cloud on a 2-Dimensional screen so the Genome Map is only a coarse approximation. It also follows that the axes of this graph have no meaning whatsoever, the only information a Genome Map conveys is which genomes are more or less similar (close together) and which genomes are more or less different (far apart).
Imagine you are an individual that has been selected for mating (yay). The population is well distributed and you are somewhere near the average (I’m sure you are a wildly original and delightful person in real life, but for the time being try to imagine you are in fact sort of average):
That red dot is you. Who looks attractive?
You could of course limit your search of potential partners to your immediate neighbourhood. This means that you mate with individuals who are very much like you and it means your offspring will also be very much like you.
When this is taken to extremes we call it incestuous mating behaviour and it can become detrimental pretty quickly. Biological incest has a nasty habit of expressing unhealthy but recessive genes, but in the digital world of Evolutionary Solvers the biggest risk of incest is a rapid decline in population diversity. Low diversity decreases the chances of finding alternate solution basins and thus it risks getting stuck in local optima.
The other extreme is to exclude everyone near you. You’ll often hear it said that opposites attract, but that’s true only up to a point. At some point the genomes at the other end of the scale become so different as to be incompatible.
This is called zoophilic mating and it can be equally detrimental. This is especially true when a population is not a single group of genomes, but in fact contains multiple sub-species, each of which is climbing their own little fitness peak.
You definitely do not want to mate with a member in a different sub-species, as the offspring would likely land somewhere in the middle. And since these two species are climbing different peaks, “in the middle” actually puts you in a fitness valley.
It would seem that the best option is to balance in-breeding and out-breeding. To select individuals that are not too close and not too far. In Galapagos you can specify an in-breeding factor (between -100% and +100%, total out-breeding vs. total in-breeding respectively) that allows you to guide this relative offset:
Note that mate selection at present completely ignores mate fitness. This is something that needs looking into for future releases, but even without any advanced selection algorithms the solver still works.