Selection Phase

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Selection Phase
At each iteration, a certain proportion of the population (or, a subset of potential design solutions) is 'selected' to 'breed' so that their features can be passed on to the next generation. Because the goal of a generative algorithm is optimization, we want it to converge high-quality traits in order to provide the best solution possible.
This value is currently fixed in Generative Design and is not yet available as a setting.
Given this, it makes sense to select only those solutions with the best possible features for breeding.
Remember that individuals with a higher fitness score have better genes (see previous section for a detailed discussion of fitness and fitness functions).
In the selection stage, selection is done on the basis of the fitness value created by the fitness function. Individuals with a higher fitness score are more likely to be selected to breed. In this way, good features are preserved in the population and passed on to future generations.
As a final note, in certain circumstances it can be exceedingly difficult - or even impossible - to define a useful fitness function. If one can be defined, we need to be able to describe it with a numerical fitness value for it to be useful.
Randomized sampling and simulation are two useful workarounds for when we faced with this challenge.
Evaluation Phase
Crossover Phase
Last modified 1yr ago
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