Editing Genetic algorithm (section)

=== Adaptive GAs ===
Genetic algorithms with adaptive parameters (adaptive genetic algorithms, AGAs) is another significant and promising variant of genetic algorithms. The probabilities of crossover (pc) and mutation (pm) greatly determine the degree of solution accuracy and the convergence speed that genetic algorithms can obtain. Researchers have analyzed GA convergence analytically.<ref>{{Cite journal |last1=Stannat |first1=W. |title=On the convergence of genetic algorithms – a variational approach |journal=Probab. Theory Relat. Fields |volume=129 |pages=113–132 |year=2004 |doi=10.1007/s00440-003-0330-y|s2cid=121086772 |doi-access=free }}</ref><ref>{{Cite journal |last1=Sharapov |first1=R.R. |last2=Lapshin |first2=A.V. |title=Convergence of genetic algorithms |journal=Pattern Recognit. Image Anal. |volume=16 |pages=392–397 |year=2006 |issue=3 |doi=10.1134/S1054661806030084|s2cid=22890010 }}</ref>

Instead of using fixed values of ''pc'' and ''pm'', AGAs utilize the population information in each generation and adaptively adjust the ''pc'' and ''pm'' in order to maintain the population diversity as well as to sustain the convergence capacity. In AGA (adaptive genetic algorithm),<ref>{{Cite journal |last1=Srinivas |first1=M. |last2=Patnaik |first2=L. |title=Adaptive probabilities of crossover and mutation in genetic algorithms |journal=IEEE Transactions on Systems, Man, and Cybernetics |volume=24 |issue=4 |pages=656–667 |year=1994 |doi=10.1109/21.286385 |url=http://eprints.iisc.ac.in/6971/2/adaptive.pdf |archive-url=https://ghostarchive.org/archive/20221009/http://eprints.iisc.ac.in/6971/2/adaptive.pdf |archive-date=2022-10-09 |url-status=live }}</ref> the adjustment of ''pc'' and ''pm'' depends on the fitness values of the solutions. There are more examples of AGA variants: Successive zooming method is an early example of improving convergence.<ref>{{Cite journal |last1=Kwon |first1=Y.D. |last2=Kwon |first2=S.B. |last3=Jin |first3=S.B. |last4=Kim |first4=J.Y. |title=Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems |journal=Computers & Structures |volume=81 |issue=17  |pages=1715–1725 |year=2003 |doi=10.1016/S0045-7949(03)00183-4}}</ref> In ''CAGA'' (clustering-based adaptive genetic algorithm),<ref>{{cite journal |last1=Zhang |first1=J. |last2=Chung |first2=H. |last3=Lo, W. L. |title=Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms |journal=IEEE Transactions on Evolutionary Computation |volume=11 |issue=3 |pages=326&ndash;335 |year=2007 |doi=10.1109/TEVC.2006.880727 |s2cid=2625150 }}</ref> through the use of clustering analysis to judge the optimization states of the population, the adjustment of ''pc'' and ''pm'' depends on these optimization states. Recent approaches use more abstract variables for deciding ''pc'' and ''pm''. Examples are dominance & co-dominance principles<ref>{{Cite journal |last1=Pavai |first1=G. |last2=Geetha |first2=T.V. |title= New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms|journal=Soft Comput  |volume=23 |pages=3661–3686  |year=2019 |issue=11 |doi=10.1007/s00500-018-3016-1|s2cid=254028984 }}</ref> and LIGA (levelized interpolative genetic algorithm), which combines a flexible GA with modified A* search to tackle search space anisotropicity.<ref>{{Cite journal |last1=Li |first1=J.C.F. |last2=Zimmerle |first2=D. |last3=Young |first3=P. |title=Flexible networked rural electrification using levelized interpolative genetic algorithm |journal=Energy & AI |volume=10 |pages=100186 |year=2022 |doi=10.1016/j.egyai.2022.100186|s2cid=250972466 |doi-access=free |bibcode=2022EneAI..1000186L }}</ref>

It can be quite effective to combine GA with other optimization methods. A GA tends to be quite good at finding generally good global solutions, but quite inefficient at finding the last few mutations to find the absolute optimum. Other techniques (such as [[Hill climbing|simple hill climbing]]) are quite efficient at finding absolute optimum in a limited region. Alternating GA and hill climbing can improve the efficiency of GA {{Citation needed|date=July 2016}} while overcoming the lack of robustness of hill climbing.

This means that the rules of genetic variation may have a different meaning in the natural case. For instance &ndash; provided that steps are stored in consecutive order &ndash; crossing over may sum a number of steps from maternal DNA adding a number of steps from paternal DNA and so on. This is like adding vectors that more probably may follow a ridge in the phenotypic landscape. Thus, the efficiency of the process may be increased by many orders of magnitude. Moreover, the [[Chromosomal inversion|inversion operator]] has the opportunity to place steps in consecutive order or any other suitable order in favour of survival or efficiency.<ref>See for instance [http://www.thisurlisfalse.com/evolution-in-a-nutshell/ Evolution-in-a-nutshell] {{Webarchive|url=https://web.archive.org/web/20160415193505/http://www.thisurlisfalse.com/evolution-in-a-nutshell/ |date=15 April 2016 }} or example in [[travelling salesman problem]], in particular the use of an [[edge recombination operator]].</ref>

A variation, where the population as a whole is evolved rather than its individual members, is known as gene pool recombination.

A number of variations have been developed to attempt to improve performance of GAs on problems with a high degree of fitness epistasis, i.e. where the fitness of a solution consists of interacting subsets of its variables. Such algorithms aim to learn (before exploiting) these beneficial phenotypic interactions. As such, they are aligned with the Building Block Hypothesis in adaptively reducing disruptive recombination. Prominent examples of this approach include the mGA,<ref>{{cite journal |url=http://www.complex-systems.com/issues/03-5.html |first1=D. E. |last1=Goldberg |first2=B. |last2=Korb |first3=K. |last3=Deb |title=Messy Genetic Algorithms : Motivation Analysis, and First Results |journal=Complex Systems |volume=5 |issue=3 |pages=493–530 |year=1989 }}</ref> GEMGA<ref>[https://www.osti.gov/servlets/purl/524858 Gene expression: The missing link in evolutionary computation]</ref> and LLGA.<ref>{{cite thesis |last=Harik |first=G. |date=1997 |title=Learning linkage to efficiently solve problems of bounded difficulty using genetic algorithms |type=PhD |publisher=Dept. Computer Science, University of Michigan, Ann Arbour |url=http://portal.acm.org/citation.cfm?id=269517 }}</ref>