Editing Genetic algorithm (section)

====Other evolutionary computing algorithms====

Evolutionary computation is a sub-field of the [[metaheuristic]] methods.

* [[Memetic algorithm]] (MA), often called ''hybrid genetic algorithm'' among others, is a population-based method in which solutions are also subject to local improvement phases. The idea of memetic algorithms comes from [[meme]]s, which unlike genes, can adapt themselves. In some problem areas they are shown to be more efficient than traditional evolutionary algorithms.
* [[Bacteriologic algorithm]]s (BA) inspired by [[evolutionary ecology]] and, more particularly, bacteriologic adaptation. Evolutionary ecology is the study of living organisms in the context of their environment, with the aim of discovering how they adapt. Its basic concept is that in a heterogeneous environment, there is not one individual that fits the whole environment. So, one needs to reason at the population level. It is also believed BAs could be successfully applied to complex positioning problems (antennas for cell phones, urban planning, and so on) or data mining.<ref>{{cite journal|url=http://www.irisa.fr/triskell/publis/2005/Baudry05d.pdf |archive-url=https://ghostarchive.org/archive/20221009/http://www.irisa.fr/triskell/publis/2005/Baudry05d.pdf |archive-date=2022-10-09 |url-status=live|first=Benoit|last=Baudry |author2=Franck Fleurey |author3-link=Jean-Marc Jézéquel|author3=Jean-Marc Jézéquel |author4=Yves Le Traon|title=Automatic Test Case Optimization: A Bacteriologic Algorithm|date=March–April 2005|pages=76–82|journal=IEEE Software|issue=2|doi=10.1109/MS.2005.30|volume=22|s2cid=3559602|access-date=9 August 2009}}</ref>
* [[Cultural algorithm]] (CA) consists of the population component almost identical to that of the genetic algorithm and, in addition, a knowledge component called the belief space.
* [[Differential evolution]] (DE) inspired by migration of superorganisms.<ref>{{cite journal|last=Civicioglu|first=P.|title=Transforming Geocentric Cartesian Coordinates to Geodetic Coordinates by Using Differential Search Algorithm|journal=Computers &Geosciences|year=2012|volume=46|pages=229–247|doi=10.1016/j.cageo.2011.12.011|bibcode=2012CG.....46..229C}}</ref>
* [[Gaussian adaptation]] (normal or natural adaptation, abbreviated NA to avoid confusion with GA) is intended for the maximisation of manufacturing yield of signal processing systems. It may also be used for ordinary parametric optimisation. It relies on a certain theorem valid for all regions of acceptability and all Gaussian distributions. The efficiency of NA relies on information theory and a certain theorem of efficiency. Its efficiency is defined as information divided by the work needed to get the information.<ref>{{cite journal|last=Kjellström|first=G.|title= On the Efficiency of Gaussian Adaptation|journal=Journal of Optimization Theory and Applications|volume=71|issue=3|pages=589–597|date=December 1991|doi= 10.1007/BF00941405|s2cid=116847975}}</ref> Because NA maximises mean fitness rather than the fitness of the individual, the landscape is smoothed such that valleys between peaks may disappear. Therefore it has a certain "ambition" to avoid local peaks in the fitness landscape. NA is also good at climbing sharp crests by adaptation of the moment matrix, because NA may maximise the disorder ([[average information]]) of the Gaussian simultaneously keeping the [[mean fitness]] constant.