Editing Fitness function (section)

== Requirements of evaluation and fitness function ==
The quality of the evaluation and calculation of a fitness function is fundamental to the success of an EA optimisation. It implements Darwin's principle of "survival of the fittest". Without fitness-based [[Selection (evolutionary algorithm)|selection]] mechanisms for mate selection and offspring acceptance, EA search would be blind and hardly distinguishable from the [[Monte Carlo method|Monte Carlo]] method. When setting up a fitness function, one must always be aware that it is about more than just describing the desired target state. Rather, the evolutionary search on the way to the optimum should also be supported as much as possible (see also section on [[#Auxiliary objectives|auxiliary objectives)]], if and insofar as this is not already done by the fitness function alone. If the fitness function is designed badly, the algorithm will either converge on an inappropriate solution, or will have difficulty converging at all.

Definition of the fitness function is not straightforward in many cases and often is performed iteratively if the fittest solutions produced by an EA is not what is desired. [[Interactive genetic algorithms]] address this difficulty by outsourcing evaluation to external agents which are normally humans.