Genetic algorithms explained

Genetic algorithms are a class of optimalisation algorithms (algorithms that help to maximise some function by adusting the input paramters) that are know for their abilety to handle large unpredicatable search spaces.

In Cumulus we employ them the bridge the gap between having a forward model (a model that can simulate a biological process given its parameters) and knowing what paramters makes this simulation fit the experimental data best. We do this because creating a backward model, one that tells us what paramters will fit a set of measurements is complicated for even simple systems, let allone for larger devices. Genetic algoritms will help us find the parameters

How genetic algorithms work

A genetic alogoritm mimics the process of population genetics in order to optimise some fitness criteron. In our case this criterion is based on how good the simulated data matches the experimental results. The following pseusocode

 selectedpopulation  = initialize()
 while stop criterion has not been met
   newpopulation       = mutate(selectedpopulation)
   newpopulationscores = evaluate(newpopulation)
   selectedpopulation  = select(newpopulation, newpopulationscores)

It repeats the following step a number of times (either a fixed number of times, or contrained by some metrix such as the fitness or the change thereof.)

Mutation

In the mutation step we add new individuals to the population. This can be done in many different ways. Classically crossingover (using values of two individuals and mixing them up into a new individual) and point mutations (adjusting a single value by a small random amount) are very popular.
In cumulus we use a method calles gausian estimation. This method assumes the optimalety surface is roughly in the shape of a n-dimensiona gausian. In the selection step we try to estimate the shape of this guasion by taking the covariance matrix of all the individuals in the population(weigthed by fitness) Then we restore our population by randomly drawing new individuals from this gausian distribution.

Evaluation

In the evaluation step yet unevaluated individuals are evaluated. In most literatures this is seen as a part of the selection step. In our system however this means the running of several model in in even more experimental settings and comparing to aquired measurements. Then combining all these comparisons into a single fitness score. It is safe to say that the brunt of our computation is consumed in this step.

Selection

In the selection step we discard some individuals of our population that we deem not good enough. For us this is as strait-forward as throwing away the worst preforming half individuals. Because

Modularety

We programmed each of the steps op our system as sepaparate object. So if you want to change any of the methods by a different algoritm replacing them is as eazy as swapping a single constuctor.