> For the complete documentation index, see [llms.txt](https://www.generativedesign.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.generativedesign.org/02-deeper-dive/02-04_genetic-algorithms/02-04-04_selection-phase.md).

# 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.

![](/files/-LrPm3WPG0lCCvzNwp-8)

Remember that individuals with a higher fitness score have better genes ([see previous section for a detailed discussion of fitness and fitness functions](/02-deeper-dive/02-04_genetic-algorithms/02-04-03_evaluation-phase.md)).

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.