Editing Fitness function (section)

==Computational efficiency==
The fitness function should not only closely align with the designer's goal, but also be computationally efficient. Execution speed is crucial, as a typical evolutionary algorithm must be iterated many times in order to produce a usable result for a non-trivial problem.

[[Fitness approximation]]<ref>{{Cite journal |last=Jin |first=Y. |date=January 2005 |title=A comprehensive survey of fitness approximation in evolutionary computation |url=http://link.springer.com/10.1007/s00500-003-0328-5 |journal=Soft Computing |language=en |volume=9 |issue=1 |pages=3–12 |doi=10.1007/s00500-003-0328-5 |s2cid=7626092 |issn=1432-7643}}</ref><ref>{{Cite journal |last1=Jin |first1=Yaochu |last2=Wang |first2=Handing |last3=Chugh |first3=Tinkle |last4=Miettinen |first4=Kaisa |date=June 2019 |title=Data-Driven Evolutionary Optimization: An Overview and Case Studies |url=https://linkinghub.elsevier.com/retrieve/pii/S2210650211000198 |journal=IEEE Transactions on Evolutionary Computation |volume=23 |issue=3 |pages=442–458 |doi=10.1109/TEVC.2018.2869001 |s2cid=55809527 |issn=1089-778X|hdl=10871/34011 |hdl-access=free }}</ref> may be appropriate, especially in the following cases: 
* Fitness computation time of a single solution is extremely high 
* Precise model for fitness computation is missing
* The fitness function is uncertain or noisy.<ref>{{Cite book |last1=Eiben |first1=A.E. |url=http://link.springer.com/10.1007/978-3-662-44874-8 |title=Introduction to Evolutionary Computing |last2=Smith |first2=J.E. |date=2015 |publisher=Springer |isbn=978-3-662-44873-1 |edition=2nd |series=Natural Computing Series |location=Berlin, Heidelberg |pages=185–194 |chapter=Nonstationary and Noisy Function Optimisation |doi=10.1007/978-3-662-44874-8 |s2cid=20912932}}</ref>
Alternatively or also in addition to the fitness approximation, the fitness calculations can also be distributed to a parallel computer in order to reduce the execution times. Depending on the [[Population model (evolutionary algorithm)|population model]] of the EA used, both the EA itself and the fitness calculations of all offspring of one generation can be executed in parallel.<ref>{{Citation |last=Sudholt |first=Dirk |title=Parallel Evolutionary Algorithms |date=2015 |url=http://link.springer.com/10.1007/978-3-662-43505-2_46 |work=Springer Handbook of Computational Intelligence |pages=929–959 |editor-last=Kacprzyk |editor-first=Janusz |place=Berlin, Heidelberg |publisher=Springer |language=en |doi=10.1007/978-3-662-43505-2_46 |isbn=978-3-662-43504-5 |access-date=2023-02-27 |editor2-last=Pedrycz |editor2-first=Witold|url-access=subscription }}</ref><ref>{{Cite book |last1=Khalloof |first1=Hatem |last2=Mohammad |first2=Mohammad |last3=Shahoud |first3=Shadi |last4=Duepmeier |first4=Clemens |last5=Hagenmeyer |first5=Veit |title=Proceedings of the 12th International Conference on Management of Digital EcoSystems |chapter=A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics |date=2020-11-02 |chapter-url=https://dl.acm.org/doi/10.1145/3415958.3433041 |language=en |location=Virtual Event United Arab Emirates |publisher=ACM |pages=124–131 |doi=10.1145/3415958.3433041 |isbn=978-1-4503-8115-4|s2cid=227179748 }}</ref><ref>{{Cite book |last=Jähne |first=Paul |url=https://subs.emis.de/LNI/Proceedings/Proceedings259/2163.pdf |title=Overview of the current state of research on parallelisation of evolutionary algorithms on graphic cards |date=2016 |publisher=Gesellschaft für Informatik, FRG |isbn=978-3-88579-653-4 |editor-last=Mayr |editor-first=Heinrich Christian |location=Bonn |language=en |oclc=962381748 |editor-last2=Pinzger |editor-first2=Martin}}</ref>