Editing Genetic algorithm (section)

== Problem domains ==
Problems which appear to be particularly appropriate for solution by genetic algorithms include [[Genetic algorithm scheduling|timetabling and scheduling problems]], and many scheduling software packages are based on GAs{{Citation needed|date=December 2011}}. GAs have also been applied to [[engineering]].<ref>Tomoiagă B, Chindriş M, Sumper A, Sudria-Andreu A, Villafafila-Robles R. [http://www.mdpi.com/1996-1073/6/3/1439/pdf Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II. ] Energies. 2013; 6(3):1439-1455.</ref> Genetic algorithms are often applied as an approach to solve [[global optimization]] problems.

As a general rule of thumb genetic algorithms might be useful in problem domains that have a complex [[fitness landscape]] as mixing, i.e., [[Mutation (genetic algorithm)|mutation]] in combination with [[Crossover (genetic algorithm)|crossover]], is designed to move the population away from [[local optima]] that a traditional [[hill climbing]] algorithm might get stuck in. Observe that commonly used crossover operators cannot change any uniform population. Mutation alone can provide [[ergodicity]] of the overall genetic algorithm process (seen as a [[Markov chain]]).

Examples of problems solved by genetic algorithms include: mirrors designed to funnel sunlight to a solar collector,<ref>{{cite web|last=Gross|first=Bill|title=A solar energy system that tracks the sun|url=https://www.ted.com/talks/bill_gross_a_solar_energy_system_that_tracks_the_sun|work=TED|date=2 February 2009 |access-date=20 November 2013}}</ref> antennae designed to pick up radio signals in space,<ref>{{citation |first1=G. S. |last1=Hornby |first2=D. S. |last2=Linden |first3=J. D. |last3=Lohn |url=http://ti.arc.nasa.gov/m/pub-archive/1244h/1244%20(Hornby).pdf |title=Automated Antenna Design with Evolutionary Algorithms}}</ref> walking methods for computer figures,<ref>{{Cite web | url=http://goatstream.com/research/papers/SA2013/index.html | title=Flexible Muscle-Based Locomotion for Bipedal Creatures}}</ref> optimal design of aerodynamic bodies in complex flowfields<ref>{{Cite journal|last1=Evans|first1=B.|last2=Walton|first2=S.P.|date=December 2017|title=Aerodynamic optimisation of a hypersonic reentry vehicle based on solution of the Boltzmann–BGK equation and evolutionary optimisation|journal=Applied Mathematical Modelling|volume=52|pages=215–240|doi=10.1016/j.apm.2017.07.024|issn=0307-904X|url=https://cronfa.swan.ac.uk/Record/cronfa34688|doi-access=free}}</ref>

In his ''Algorithm Design Manual'', [[Steven Skiena|Skiena]] advises against genetic algorithms for any task:

{{blockquote|[I]t is quite unnatural to model applications in terms of genetic operators like mutation and crossover on bit strings. The pseudobiology adds another level of complexity between you and your problem. Second, genetic algorithms take a very long time on nontrivial problems. [...] [T]he analogy with evolution—where significant progress require [sic] millions of years—can be quite appropriate.
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I have never encountered any problem where genetic algorithms seemed to me the right way to attack it. Further, I have never seen any computational results reported using genetic algorithms that have favorably impressed me. Stick to [[simulated annealing]] for your heuristic search voodoo needs.|Steven Skiena<ref>{{cite book |last=Skiena |first=Steven |author-link=Steven Skiena |title = The Algorithm Design Manual |publisher=[[Springer Science+Business Media]] |edition=2nd |year = 2010 |isbn=978-1-849-96720-4}}</ref>{{rp|267}}}}