> 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/02-04-03_evaluation-phase.md).

# Evaluation Phase

## What Do You Mean by ‘Fitness’?

A 'fitness' function is essentially the objective function for the genetic algorithm - it's the thing you want to maximize or minimize as you develop your design; the thing you care most about achieving overall.

A fitness function is used to evaluate how close (or far off) a given design solution is from meeting the designer’s goals.

The genetic algorithm is designed to improve the model's fitness again and again, so defining a fitness function precisely and accurately is vital.

![](/files/-LrPm3A_ELFtx1qwBLCI)

Some examples of fitness functions that could be used in a generative design context include:

* 'Maximize hours of daylight for each desk in an office'. 
* 'Maximize circulation in common areas'.
* 'Minimize number of distinct part types needed to assemble an object'.
* 'Minimize number of total parts needed to assemble an object'. 
* 'Maximize the structural strength of a critical component in a product'.  
* 'Minimize the fuel needed to power a vehicle'. 
* 'Minimize the weight of a design'. 

You can see here that these suggestions are always framed as either a maximization or minimization problem. As discussed above, this formulation is crucial for an optimization approach to be effective.

![](/files/-LrPm3AbB26vN2x51uSf)

Another thing to consider is that it's desirable for fitness functions to be calculated very efficiently by a computer; that is, a good fitness function can be calculated quickly. With experience, a user comes to learn which kinds of fitness functions are likely to be particularly fast or slow.

One of the great strengths of a genetic approach is that the fitness function can be quite complicated without impacting the genetic algorithm's ability to execute.

In fact, a single genetic model can have multiple competing fitness functions for example, minimizing the weight of a design while also making it as structurally sound as possible (this is also known as 'multi-objective optimization').

![](/files/-LrPm3AdzV0fG65jeCBA)

Only once a fitness function has been defined can the selection phase of a genetic algorithm begin.