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=== Chromosomes for complex representations ===
The chromosomes presented above are well suited for processing tasks of continuous, mixed-integer, pure-integer or combinatorial optimization. For a combination of these optimization areas, on the other hand, it becomes increasingly difficult to map them to simple strings of values, depending on the task. The following extension of the gene concept is proposed by the EA GLEAM (General Learning Evolutionary Algorithm and Method) for this purpose:<ref name=":4">{{Citation |last1=Blume |first1=Christian |last2=Jakob |first2=Wilfried |title=GLEAM - An Evolutionary Algorithm for Planning and Control Based on Evolution Strategy |date=2002 |url=https://publikationen.bibliothek.kit.edu/170053025/3814288 |work=Conf. Proc. of Genetic and Evolutionary Computation Conference (GECCO 2002) |volume=Late Breaking Papers |pages=31–38 |access-date=2023-01-01 }}</ref> A gene is considered to be the description of an element or elementary trait of the phenotype, which may have multiple parameters. For this purpose, gene types are defined that contain as many parameters of the appropriate data type as are required to describe the particular element of the phenotype. A chromosome now consists of genes as data objects of the gene types, whereby, depending on the application, each gene type occurs exactly once as a gene or can be contained in the chromosome any number of times. The latter leads to chromosomes of dynamic length, as they are required for some problems.<ref>{{Cite journal |last1=Pawar |first1=Sunil Nilkanth |last2=Bichkar |first2=Rajankumar Sadashivrao |date=June 2015 |title=Genetic algorithm with variable length chromosomes for network intrusion detection |url=http://link.springer.com/10.1007/s11633-014-0870-x |journal=International Journal of Automation and Computing |language=en |volume=12 |issue=3 |pages=337–342 |doi=10.1007/s11633-014-0870-x |s2cid=255346767 |issn=1476-8186|doi-access=free }}</ref><ref>{{Citation |last=Blume |first=Christian |title=Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software GLEAM |date=2000 |url=http://link.springer.com/10.1007/3-540-45561-2_32 |work=Real-World Applications of Evolutionary Computing |series=Lecture Notes in Computer Science |volume=1803 |pages=330–341 |editor-last=Cagnoni |editor-first=Stefano |access-date=2023-06-25 |place=Berlin, Heidelberg |publisher=Springer Berlin Heidelberg |language=en |doi=10.1007/3-540-45561-2_32 |isbn=978-3-540-67353-8}}</ref> The gene type definitions also contain information on the permissible value ranges of the gene parameters, which are observed during chromosome generation and by corresponding mutations, so they cannot lead to lethal mutations. For tasks with a combinatorial part, there are suitable [[Genetic operator|genetic operators]] that can move or reposition genes as a whole, i.e. with their parameters.
[[File:Genmodell Chromosombeispiel.png|thumb|212x212px|Three exemplary genes matching the adjacent gene type definitions in a chromosome organized as a list]]
[[File:Gene model gene types.png|left|thumb|224x224px|Three exemplary genes matching the adjacent gene type definitions in a chromosome organized as a list]]
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